Publications

Tariq, Amara, Man Luo, Aisha Urooj, Avisha Das, Jiwoong Jeong, Shubham Trivedi, Haidar Abdul-Muhsin et al. "Development of LLM for Prostate Cancer—The Need for Domain-Tailored Training." In Cancer Detection and Diagnosis: A Handbook of Emerging Technologies, pp. 397-406. CRC Press, 2025.

Tariq, Amara, Avisha Das, Fatima-Zahrae Nakach, Nathan Yu, Bhavik Patel, and Imon Banerjee. "Two-Phase Framework for Clinical Question-Answering: Auto-correction for Guideline-Concordance." In Generative AI in Healthcare: Transforming Diagnostics, Treatment, and Patient Outcomes, pp. 175-183. Cham: Springer Nature Switzerland, 2025.

De Cecco, C. N., Van Assen, M., & Leiner, T. (Eds.). (2022). Artificial intelligence in cardiothoracic imaging. Springer Nature.

Das, Susmita, Amara Tariq, Thiago Santos, Sai Sandeep Kantareddy, and Imon Banerjee. “Recurrent Neural Networks (RNNs): Architectures, Training Tricks, and Introduction to Influential Research.” In Machine Learning for Brain Disorders, pp. 117-138. New York, NY: Springer US, 2023.

Imon Banerjee, Chiara Eva Catalano, Francesco Robbiano, Michela Spagnuolo, “Accessing and representing knowledge in the medical field: integration perspectives”, 3D Multiscale Physiological Human, Springer London, pp: 297-316, https://doi.org/10.1007/978-1-4471-6275-9 13, (2014).

1. Tariq, Amara, Kris Goddard, Praneetha Elugunti, Kristina Piorkowski, Jared Staal, Allison Viramontes, Imon Banerjee, and Bhavik N. Patel. “Contrastive diagnostic embedding (CDE) model for automated coding–A case study using emergency department encounters.” International Journal of Medical Informatics 179 (2023): 105212.
2. Jeong, Jiwoong, Andrew Wentland, Domenico Mastrodicasa, Ghaneh Fananapazir, Adam Wang, Imon Banerjee, and Bhavik N. Patel. “Synthetic dual-energy CT reconstruction from single-energy CT Using artificial intelligence.” Abdominal Radiology (2023): 1-13.
3. Jeong, Jiwoong, Chieh-Ju Chao, Reza Arsanjani, Kihong Kim, Melissa N. Pelkey, Yi-Chieh Chen, Raheel N. Ramzan et al. “Challenges and solutions of echocardiography generalization for deep learning: a study in patients with constrictive pericarditis.” Journal of Medical Imaging 10, no. 5 (2023): 054502-054502.
4. Tariq, Amara, Lin Lancaster, Praneetha Elugunti, Eric Siebeneck, Katherine Noe, Bijan Borah, James Moriarty, Imon Banerjee, and Bhavik N. Patel. “Graph convolutional network-based fusion model to predict risk of hospital acquired infections.” Journal of the American Medical Informatics Association 30, no. 6 (2023): 1056-1067.
5. Chao, Chieh-Ju, Timothy Barry, Amith Seri, Ahmed El Shaer, Nadia Chavez Ponce, Soham Chakraborty, Sean Smith et al. “Topological Data Analysis Identified Prognostically-Distinct Phenotypes in Transcatheter Edge-to-Edge Repair Patients.” Mayo Clinic Proceedings: Digital Health 1, no. 3 (2023): 381-392.
6. El Haji, Hasna, Amine Souadka, Bhavik N. Patel, Nada Sbihi, Gokul Ramasamy, Bhavika K. Patel, Mounir Ghogho, and Imon Banerjee. “Evolution of Breast Cancer Recurrence Risk Prediction: A Systematic Review of Statistical and Machine Learning–Based Models.” JCO Clinical Cancer Informatics 7 (2023): e2300049.
7. Banerjee, Imon, Kamanasish Bhattacharjee, John L. Burns, Hari Trivedi, Saptarshi Purkayastha, Laleh Seyyed-Kalantari, Bhavik N. Patel, Rakesh Shiradkar, and Judy Gichoya. ““Shortcuts” causing bias in radiology artificial intelligence: causes, evaluation and mitigation.” Journal of the American College of Radiology (2023).
8. Li, Hanzhou, John T. Moon, Deepak Iyer, Patricia Balthazar, Elizabeth A. Krupinski, Zachary L. Bercu, Janice M. Newsome, Imon Banerjee, Judy W. Gichoya, and Hari M. Trivedi. “Decoding radiology reports: Potential application of OpenAI ChatGPT to enhance patient understanding of diagnostic reports.” Clinical Imaging (2023).
9. Hwang, InChan, Hari Trivedi, Beatrice Brown-Mulry, Linglin Zhang, Vineela Nalla, Aimilia Gastounioti, Judy Gichoya, Laleh Seyyed-Kalantari, Imon Banerjee, and MinJae Woo. “Impact of multi-source data augmentation on performance of convolutional neural networks for abnormality classification in mammography.” Frontiers in Radiology 3 (2023): 1181190.
10. Guo, Xiaoyuan, Judy Wawira Gichoya, Hari Trivedi, Saptarshi Purkayastha, and Imon Banerjee. “MedShift: Automated Identification of Shift Data for Medical Image Dataset Curation.” IEEE Journal of Biomedical and Health Informatics(2023).
11. Tariq, Amara, Bhavik N Patel, William F Sensakovic, Samuel J Fahrenholtz, and Imon Banerjee. “Opportunistic screening for low bone density using abdominopelvic computed tomography scans.” Medical physics (2023).
12. Tang, Siyi, Amara Tariq, Jared A. Dunnmon, Umesh Sharma, Praneetha Elugunti, Daniel L. Rubin, Bhavik N. Patel, and Imon Banerjee. “Predicting 30-day all-cause hospital readmission using multimodal spatiotemporal graph neural networks.” IEEE Journal of Biomedical and Health Informatics(2023).
13. Ozcan, B. Bersu, Bhavika K. Patel, Imon Banerjee, and Basak E. Dogan. “Artificial Intelligence in Breast Imaging: Challenges of Integration Into Clinical Practice.” Journal of Breast Imaging(2023): wbad007.
14. Sadasivuni, Sudarsan, Monjoy Saha, Sumukh Prashant Bhanushali, Imon Banerjee, and Arindam Sanyal. “In-sensor artificial intelligence and fusion with electronic medical records for at-home monitoring.” IEEE Transactions on Biomedical Circuits and Systems (2023).
15. Barry, Timothy, Juan Maria Farina, Chieh-Ju Chao, Chadi Ayoub, Jiwoong Jeong, Bhavik N. Patel, Imon Banerjee, and Reza Arsanjani. “The Role of Artificial Intelligence in Echocardiography.” Journal of Imaging 9, no. 2 (2023): 50.
16. Li, James, Chieh-Ju Chao, Jiwoong Jason Jeong, Juan Maria Farina, Amith R. Seri, Timothy Barry, Hana Newman et al. “Developing an Echocardiography-Based, Automatic Deep Learning Framework for the Differentiation of Increased Left Ventricular Wall Thickness Etiologies.” Journal of Imaging 9, no. 2 (2023): 48.
17. Hwang, InChan, Hari Trivedi, Beatrice Brown-Mulry, Linglin Zhang, Vineela Nalla, Aimilia Gastounioti, Judy Gichoya, Laleh Seyyed-Kalantari, Imon Banerjee, and MinJae Woo. “Impact of multi-source data augmentation on performance of convolutional neural networks for abnormality classification in mammography.” Frontiers in Radiology 3 (2023): 1181190.
18. Li, Hanzhou, John T. Moon, Deepak Iyer, Patricia Balthazar, Elizabeth A. Krupinski, Zachary L. Bercu, Janice M. Newsome, Imon Banerjee, Judy W. Gichoya, and Hari M. Trivedi. “Decoding radiology reports: Potential application of OpenAI ChatGPT to enhance patient understanding of diagnostic reports.” Clinical Imaging (2023).
19. Ozcan, B. Bersu, Bhavika K. Patel, Imon Banerjee, and Basak E. Dogan. “Artificial intelligence in breast imaging: challenges of integration into clinical practice.” Journal of Breast Imaging5, no. 3 (2023): 248-257.

2022

1. Jeong, Jiwoong J., Brianna L. Vey, Ananth Bhimireddy, Thomas Kim, Thiago Santos, Ramon Correa, Raman Dutt et al. “The EMory BrEast imaging Dataset (EMBED): A racially diverse, granular dataset of 3.4 million screening and diagnostic mammographic images.” Radiology: Artificial Intelligence 5, no. 1 (2023): e220047.
2. Gichoya, Judy Wawira, Imon Banerjee, Ananth Reddy Bhimireddy, John L. Burns, Leo Anthony Celi, Li-Ching Chen, Ramon Correa et al. “AI recognition of patient race in medical imaging: a modelling study.” The Lancet Digital Health (2022).
3. Tariq, Amara, Omar Kallas, Patricia Balthazar, Scott Jeffery Lee, Terry Desser, Daniel Rubin, Judy Wawira Gichoya, and Imon Banerjee. “Transfer language space with similar domain adaptation: a case study with hepatocellular carcinoma.” Journal of Biomedical Semantics 13, no. 1 (2022): 1-12.
4. Sadasivuni, Sudarsan, Monjoy Saha, Neal Bhatia, Imon Banerjee, and Arindam Sanyal. “Fusion of fully integrated analog machine learning classifier with electronic medical records for real-time prediction of sepsis onset.” Scientific reports 12, no. 1 (2022): 1-11.
5. Wei, Duo Helen, Polina V. Kukhareva, Donghua Tao, Margarita Sordo, Deepti Pandita, Prerna Dua, Imon Banerjee, and Joanna Abraham. “Assessing perceived effectiveness of career development efforts led by the women in American Medical Informatics Association Initiative.” Journal of the American Medical Informatics Association (2022).
6. Gichoya, Judy W., Priyanshu Sinha, Melissa Davis, Jeffrey W. Dunkle, Scott A. Hamlin, Keith D. Herr, Carrie N. Hoff et al. “Multireader evaluation of radiologist performance for COVID-19 detection on emergency department chest radiographs.” Clinical Imaging 82 (2022): 77-82.
7. Jeong, Jiwoong J., Amara Tariq, Tobiloba Adejumo, Hari Trivedi, Judy W. Gichoya, and Imon Banerjee. “Systematic Review of Generative Adversarial Networks (GANs) for Medical Image Classification and Segmentation.” Journal of Digital Imaging (2022): 1-16.
8. Kandaswamy, Swaminathan, Evan Orenstein, Elizabeth Mary Quincer, Alfred Fernandez, Mark Gonzalez, Lydia Lu, Rishikesan Kamaleswaran, Imon Banerjee, and Preeti Jaggi. “Automated Identification of Immunocompromised Status in Critically Ill Children.” Methods of Information in Medicine AAM (2022).
9. Dutt, Raman, Dylan Mendonca, Ming Phen, Samuel Broida, Marzyeh Ghassemi, Judy Gichoya, Imon Banerjee, Tim Yoon, and Hari Trivedi. “Automatic Localization and Brand Detection of Cervical Spine Hardware on Radiographs Using Weakly Supervised Machine Learning.” Radiology: Artificial Intelligence (2022): e2100
10. Tadavarthi, Yasasvi, Valeria Makeeva, William Wagstaff, Henry Zhan, Anna Podlasek, Neil Bhatia, Marta Heilbrun et al. “Overview of Noninterpretive Artificial Intelligence Models for Safety, Quality, Workflow, and Education Applications in Radiology Practice.” Radiology: Artificial Intelligence 4, no. 2 (2022).
11. Kathiravelu, Pradeeban, Zachary Zaiman, Judy Gichoya, Luís Veiga, and Imon Banerjee. “Towards an internet-scale overlay network for latency-aware decentralized workflows at the edge.” Computer Networks 203 (2022): 108654.
12. Yala, Adam, Peter G. Mikhael, Constance Lehman, Gigin Lin, Fredrik Strand, Yung-Liang Wan, Kevin Hughes et al. “Optimizing risk-based breast cancer screening policies with reinforcement learning.” Nature medicine (2022): 1-8.
13. Davis, Melissa A., Judy Wawira Gichoya, Imon Banerjee, Declan Sung, Janice Newsome, Brianna L. Vey, Roger Gerard et al. “Balancing the Scales: An Analysis of Social Determinants of Health, Radiology Report Acuity, and Radiology Staffing Models in an Academic Health System.” Journal of the American College of Radiology 19, no. 1 (2022): 172-177.
14. Foran, David J., Eric B. Durbin, Wenjin Chen, Evita Sadimin, Ashish Sharma, Imon Banerjee, Tahsin Kurc et al. “An expandable informatics framework for enhancing central cancer registries with digital pathology specimens, computational imaging tools, and advanced mining capabilities.” Journal of Pathology Informatics 13, no. 1 (2022): 5.
15. Gordon, Alexandra June, Imon Banerjee, Jason Block, Christopher Winstead-Derlega, Jennifer G. Wilson, Tsuyoshi Mitarai, Michael Jarrett et al. “Natural language processing of head CT reports to identify intracranial mass effect: CTIME algorithm.” The American journal of emergency medicine 51 (2022): 388-392.
16. Sanyal, Josh, Daniel Rubin, and Imon Banerjee. “A weakly supervised model for the automated detection of adverse events using clinical notes.” Journal of Biomedical Informatics126 (2022): 103969.

2021

1. Sanyal, Josh, Daniel Rubin, and Imon Banerjee. “A weakly supervised model for the automated detection of adverse events using clinical notes.” Journal of biomedical informatics(2021): 103969.
2. Gichoya, Judy W., Priyanshu Sinha, Melissa Davis, Jeffrey W. Dunkle, Scott A. Hamlin, Keith D. Herr, Carrie N. Hoff et al. “Multireader evaluation of radiologist performance for COVID-19 detection on emergency department chest radiographs.” Clinical Imaging (2021).
3. Gordon, Alexandra June, Imon Banerjee, Jason Block, Christopher Winstead-Derlega, Jennifer G. Wilson, Tsuyoshi Mitarai, Michael Jarrett, Daniel L. Rubin, Max Wintermark, and Michael A. Kohn. “Natural language processing of head CT reports to identify intracranial mass effect: CTIME algorithm.” The American Journal of Emergency Medicine (2021).
4. Yala, Adam, Peter G. Mikhael, Fredrik Strand, Gigin Lin, Siddharth Satuluru, Thomas Kim, Imon Banerjee et al. “Multi-Institutional Validation of a Mammography-Based Breast Cancer Risk Model.” Journal of Clinical Oncology (2021): JCO-21.
5. Wang, Yibo, Amara Tariq, Fiza Khan, Judy Wawira Gichoya, Hari Trivedi, and Imon Banerjee. “Query bot for retrieving patients’ clinical history: a COVID-19 use-case.” Journal of Biomedical Informatics 123 (2021): 103918.
6. Tariq, Amara, Marly Van Assen, Carlo N. De Cecco, and Imon Banerjee. “Bridging the Gap between Structured and Free-form Radiology Reporting: A Case-study on Coronary CT Angiography.” ACM Transactions on Computing for Healthcare (HEALTH) 3, no. 1 (2021): 1-20.
7. Tariq, Amara, Leo Anthony Celi, Janice M. Newsome, Saptarshi Purkayastha, Neal Kumar Bhatia, Hari Trivedi, Judy Wawira Gichoya, and Imon Banerjee. “Patient-specific COVID-19 resource utilization prediction using fusion AI model.” NPJ digital medicine 4, no. 1 (2021): 1-9.
8. Wang, Yibo, Amara Tariq, Fiza Khan, Judy Wawira Gichoya, Hari Trivedi, and Imon Banerjee. “Query bot for retrieving patients’ clinical history: a COVID-19 use-case.” Journal of Biomedical Informatics (2021): 103918.
9. Guo, Xiaoyuan, W. Charles O’Neill, Brianna Vey, Tianen Christopher Yang, Thomas J. Kim, Maryzeh Ghassemi, Ian Pan, Judy Wawira Gichoya, Hari Trivedi, and Imon Banerjee. “SCU‐Net: A deep learning method for segmentation and quantification of breast arterial calcifications on mammograms.” Medical Physics (2021).
10. Karimi, Yasmin H., Allison W. Kurian, Douglas W. Blayney, and Imon Banerjee. “Reply to Ritzwoller et al.” JCO clinical cancer informatics 5 (2021): 1026-1027.
11. Sadasivuni, Sudarsan, Monjoy Saha, Neal Bhatia, Imon Banerjee, and Arindam Sanyal. “Fusion of Fully Integrated Analog Machine Learning Classifier with Electronic Medical Records for Real-Time Prediction of Sepsis Onset.” (2021).
12. Clayton, Eric J., Imon Banerjee, Patrick J. Ward, Maggie D. Howell, Beth Lohmueller, Sunita Pierson, Rachel Hall, Peter B. Harrison, and David Michael Waterhouse. “A natural language processing tool for automatic identification of new disease and disease progression: Parsing text in multi-institutional radiology reports to facilitate clinical trial eligibility screening.” (2021): 1555-1555.
13. Sanyal, Josh, Amara Tariq, Allison W. Kurian, Daniel Rubin, and Imon Banerjee. “Weakly supervised temporal model for prediction of breast cancer distant recurrence.” Scientific Reports 11, no. 1 (2021): 1-11.
14. Karimi, Yasmin H., Douglas W. Blayney, Allison W. Kurian, Jeanne Shen, Rikiya Yamashita, Daniel Rubin, and Imon Banerjee. “Development and Use of Natural Language Processing for Identification of Distant Cancer Recurrence and Sites of Distant Recurrence Using Unstructured Electronic Health Record Data.” JCO Clinical Cancer Informatics 5 (2021): 469-478.
15. Jiaming Zeng, Imon Banerjee, A. Solomon Henry, Douglas J. Wood, Ross D. Shachter, Michael F. Gensheimer, and Daniel L. Rubin. “Natural Language Processing to Identify Cancer Treatments With Electronic Medical Records.” JCO Clinical Cancer Informatics 5 (2021): 379-393.
16. Sun, Ran, Imon Banerjee, Shengtian Sang, Jennifer Joseph, Jennifer Schneider, and Tina Hernandez-Boussard. “Type 1 Diabetes Management With Technology: Patterns of Utilization and Effects on Glucose Control Using Real-World Evidence.” Clinical Diabetes (2021).
17. Chaves, J.M.Z., Chaudhari, A.S., Wentland, A.L., Banerjee, I., Boutin, R.D., Maron, D.J., Rodriguez, F., Sandhu, A.T., Jeffrey, R.B., Rubin, D. and Patel, B., 2021. Opportunistic Assessment of Ischemic Heart Disease Risk Using Abdominopelvic Computed Tomography and Medical Record Data: a Multimodal Explainable Artificial Intelligence Approach. medRxiv.
18. Chandrasekaran, Sanjeev Tannirkulam, Akshay Jayaraj, Vinay Elkoori Ghantala Karnam, Imon Banerjee, and Arindam Sanyal. “Fully Integrated Analog Machine Learning Classifier Using Custom Activation Function for Low Resolution Image Classification.” IEEE Transactions on Circuits and Systems I: Regular Papers (2021).
19. Kathiravelu, Pradeeban, Puneet Sharma, Ashish Sharma, Imon Banerjee, Hari Trivedi, Saptarshi Purkayastha, Priyanshu Sinha, Alexandre Cadrin-Chenevert, Nabile Safdar, and Judy Wawira Gichoya. “A DICOM Framework for Machine Learning and Processing Pipelines Against Real-time Radiology Images.” Journal of Digital Imaging 34, no. 4 (2021): 1005-1013.
20. Guo, Xiaoyuan, Judy Wawira Gichoya, Hari Trivedi, Saptarshi Purkayastha, and Imon Banerjee. “MedShift: identifying shift data for medical dataset curation.” arXiv preprint arXiv:2112.13885 (2021).
21. Kathiravelu, Pradeeban, Zachary Zaiman, Judy Gichoya, Luís Veiga, and Imon Banerjee. “Towards an internet-scale overlay network for latency-aware decentralized workflows at the edge.” Computer Networks (2021): 108654.
22. Sanyal, Josh, Daniel Rubin, and Imon Banerjee. “A weakly supervised model for the automated detection of adverse events using clinical notes.” Journal of biomedical informatics(2021): 103969.
23. Chandrasekaran, Sanjeev Tannirkulam, Imon Banerjee, and Arindam Sanyal. “7.5 nJ/inference CMOS Echo State Network for Coronary Heart Disease prediction.” In ESSDERC 2021-IEEE 51st European Solid-State Device Research Conference (ESSDERC), pp. 103-106. IEEE, 2021.
24. Sadasivuni, Sudarsan, Sumukh Prashant Bhanushali, Sai Srinivasa Singamsetti, Imon Banerjee, and Arindam Sanyal. “Multi-Task Learning Mixed-Signal Classifier for In-situ Detection of Atrial Fibrillation and Sepsis.” In 2021 IEEE Biomedical Circuits and Systems Conference (BioCAS), pp. 1-4. IEEE, 2021.
25. Chandrasekaran, Sanjeev Tannirkulam, Sumukh Prashant Bhanushali, Imon Banerjee, and Arindam Sanyal. “Toward Real-Time, At-Home Patient Health Monitoring Using Reservoir Computing CMOS IC.” IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11, no. 4 (2021): 829-839.
26. Guo, Xiaoyuan, Judy Wawira Gichoya, Saptarshi Purkayastha, and Imon Banerjee. “CVAD/An unsupervised image anomaly detector.” Software Impacts (2021): 100195.

2020

1. Huang, Shih-Cheng, Anuj Pareek, Saeed Seyyedi, Imon Banerjee, and Matthew P. Lungren. “Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines.” NPJ digital medicine 3, no. 1 (2020): 1-9.
2. Imon Banerjee, Luis de Sisternes, Joelle A. Hallak, Theodore Leng, Aaron Osborne, Philip J. Rosenfeld, Giovanni Gregori, Mary Durbin, Daniel Rubin, “Prediction of age-related macular degeneration disease using a sequential deep learning approach on longitudinal SD-OCT imaging biomarkers”, Nature Scientific Reports (2020).
3. Bozkurt, Selen, Rohan Paul, Jean Coquet, Ran Sun, Imon Banerjee, James D. Brooks, and Tina Hernandez‐Boussard. “Phenotyping severity of patient‐centered outcomes using clinical notes: A prostate cancer use case.” Learning health systems 4, no. 4 (2020): e10237.
4. Sumukh Prashant Bhanushali, Sudarsan Sadasivuni, Imon Banerjee, and Arindam Sanyal. “Digital Machine Learning Circuit for Real-Time Stress Detection from Wearable ECG Sensor.” In 2020 IEEE 63rd International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 978-981. IEEE, 2020.
5. Amara Tariq, Saptarshi Purkayastha, Geetha P. Padmanaban, Elizabeth Krupinski, Hari Trivedi, Imon Banerjee, and Judy W. Gichoya. “Current clinical applications of AI in Radiology and their best-supporting evidence.” Journal of the American College of Radiology (2020).
6. Sanjeev Tannirkulam Chandrasekaran, Sumukh Prashant Bhanushali, Imon Banerjee, and Arindam Sanyal. “A Bio-Inspired Reservoir-Computer for Real-Time Stress Detection from ECG Signal.” IEEE Solid-State Circuits Letters (2020).
7. Shih-Cheng Huang, Anuj Pareek, Roham Zamanian, Imon Banerjee, Matthew P Lungren, “Multimodal Fusion with Deep Neural Networks for Leveraging CT Imaging and Electronic Health Record – A Case-study in Pulmonary Embolism Detection”, Nature Scientific Reports, 2020. (accepted)
8. Scott J. Lee,Brent D. Weinberg, Ashwani Gore, Imon Banerjee, “A Scalable Natural Language Processing for Inferring BT-RADS Categorization from Unstructured Brain Magnetic Resonance Reports”, Journal of Digital Imaging, 2020.
9. Shih-Cheng Huang, Tanay Kothari, Imon Banerjee, Chris Chute, Robyn L Ball, Norah Borus, Andrew Huang, Bhavik N Patel, Pranav Rajpurkar, Jeremy Irvin, Jared Dunnmon, Joseph Bledsoe, Katie Shpanskaya, Abhay Dhaliwal, Roham Zamanian, Andrew Y Ng, Matthew P Lungren, “PENet—a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging”, npj Digital Medicine, 2020.
10. Imon Banerjee, Priyanshu Sinha, Saptarshi Purkayastha, Nazanin Mashhaditafreshi, Amara Tariq, Jiwoong Jeong, Hari Trivedi, and Judy W. Gichoya. “Was there COVID-19 back in 2012? Challenge for AI in Diagnosis with Similar Indications.” arXiv preprint arXiv:2006.13262 (2020).
11. Scott J. Lee, Brent D. Weinberg, Ashwani Gore, and Imon Banerjee. “A Scalable Natural Language Processing for Inferring BT-RADS Categorization from Unstructured Brain Magnetic Resonance Reports.” Journal of Digital Imaging(2020).
12. van Assen, Marly, Imon Banerjee, and Carlo N. De Cecco. “Beyond the artificial intelligence hype: what lies behind the algorithms and what we can achieve.” Journal of Thoracic Imaging 35 (2020): S3-S10.
13. Huang, Shih-Cheng, Tanay Kothari, Imon Banerjee, Chris Chute, Robyn L. Ball, Norah Borus, Andrew Huang et al. “PENet—a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging.” npj Digital Medicine 3, no. 1 (2020): 1-9.
14. Chandrasekaran, Sanjeev T., Ruobing Hua, Imon Banerjee, and Arindam Sanyal. “A Fully-Integrated Analog Machine Learning Classifier for Breast Cancer Classification.” Electronics 9, no. 3 (2020): 515.
15. Li, Kevin, Imon Banerjee, Christopher J. Magnani, Douglas W. Blayney, James D. Brooks, and Tina Hernandez-Boussard. “Clinical Documentation to Predict Factors Associated with Urinary Incontinence Following Prostatectomy for Prostate Cancer.” Research and Reports in Urology 12 (2020):

2019

1. Imon Banerjee, Selen Bozkurt, Jennifer Lee Caswell-Jin, Allison W. Kurian, and Daniel L. Rubin. “Natural Language Processing Approaches to Detect the Timeline of Metastatic Recurrence of Breast Cancer.” JCO Clinical Cancer Informatics (2019).
2. Imon Banerjee, Miji Sofela, Jaden Yang, Jonathan H. Chen, Nigam H. Shah, Robyn Ball, Alvin I. Mushlin et al. “Development and Performance of the Pulmonary Embolism Result Forecast Model (PERFORM) for Computed Tomography Clinical Decision Support.” JAMA network open2, no. 8 (2019): e198719-e198719.
3. Davide Gori, Imon Banerjee, Benjamin I. Chung, Michelle Ferrari, Paola Rucci, Douglas W. Blayney, James D. Brooks, Tina Hernandez-Boussard, “Extracting Patient-Centered Outcomes from Clinical Notes in Electronic Health Records: Assessment of Urinary Incontinence After Radical Prostatectomy”, eGEMS (2019).
4. Imon Banerjee, Selen Bozkurt, Emel Alkim, Hersh Sagreiya, Allison W. Kurian, Daniel L. Rubin, “Automatic Inference of BIRADS Final Assessment Categories from Narrative Mammography Report Findings, Journal of Biomedical Informatics”, (2019).
5. Imon Banerjee, Selen Bozkurt, Emel Alkim, Daniel L. Rubin, “Automated Detection of Measurements and Their Descriptors in Radiology Reports Using a Hybrid Natural Language Processing Algorithm”, Journal of Digital Imaging (2019).
6. Arjun Parthipan, Imon Banerjee, Keith Humphryes, Steve M. Asch, Daniel Rubin, Catherine Curtin, Ian Carroll, Tina Hernandez-Boussard, “Predicting Inadequate Postoperative Pain Management in Depressed Patients: A Machine Learning Approach”, Plos one.
7. Imon Banerjee, Kevin Li, Martin Seneviratne, Michelle Ferrari, Tina Seto, James D. Brooks, Daniel L. Rubin, Tina Hernandez-Boussard, Weakly supervised natural language processing for assessing treatment-related side effects following prostate cancer treatment, JAMIA Open, ooy057, https://doi.org/10.1093/jamiaopen/ooy057, (2019).

2018

1. Imon Banerjee, Camille Kurtz, Alon Edward Devorah, Bao Do, Daniel L. Rubin, Christopher F. Beaulieu, Relevance Feedback for Enhancing Content Based Image Retrieval and Automatic Prediction of Semantic Image Features: Application to Bone Tumor Radiographs, Journal of Biomedical Informatics, 84, pp.123-135, doi:https://doi.org/10.1016/j.jbi.2018.07.002, (2018).
2. Imon Banerjee, Matthew C. Chen, Yuan Ling, Sadid A. Hasan, Curtis P. Langlotz, Nathaniel Moradzadeh, Brian Chapman, Timothy Amrhein, David Mong, Daniel L. Rubin, Oladimeji Farri, Matthew P. Lungren, Comparative E ectiveness of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) Architectures for Radiology Text Report Classification, Journal of Artificial Intelligence in Medicine, https://doi.org/10.1016/j.artmed.2018.11, (2018).
3. Jayaraj, Akshay, Sanjeev Tannirkulam Chandrasekaran, Archana Ganesh, Imon Banerjee, Arindam Sanyal, Maximum Likelihood Estimation Based SAR ADC, IEEE Transactions on Circuits and Systems II: Express Briefs, https://doi.org/10.1109/TCSII.2018.2886260, (2018).
4. Imon Banerjee, Michael Francis Gensheimer, Douglas J. Wood, Solomon Henry, Sonya Aggarwal, Daniel T. Chang, Daniel L. Rubin, Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) Utilizing Free-Text Clinical Narratives, Nature Scientific Reports, volume 8, Article number: 10037 (2018)
5. Michael F. Gensheimer, Sonya Aggarwal, Sara A. Dudley, A. Solomon Henry, Douglas J.Wood, Trevor J. Hastie, Imon Banerjee, Eunpi Cho, Pooja Pradhan, Albert C. Koong, Daniel L. Rubin, Daniel T. Chang, Large-Scale Machine Learning for Survival Prediction in Metastatic Cancer Patients, JNCI: Journal of the National Cancer Institute, djy178, https://doi.org/10.1093/jnci/djy178, (2018).
6. Brian D. Piening, Wenyu Zhou, Kevin Contrepois, Hannes Rst, Gucci Jijuan Gu Urban, Tejaswini Mishra, Blake M. Hanson, Eddy J. Bautista, Shana Leopold, Christine Y. Yeh, Daniel Spakowicz, Imon Banerjee, Cynthia Chen, Kimberly Kukurba, Dalia Perelman, Colleen Craig, Elizabeth Colbert, Denis Salins, Shannon Rego, Sunjae Lee, Cheng Zhang, Jessica Wheeler, M. Reza Sailani, Liang Liang, Charles Abbott, Mark Gerstein, Adil Mardinoglu, Ulf Smith, Daniel L. Rubin Sharon Pitteri, Erica Sodergren, Tracey L. McLaughlin, George M. Weinstock, Michael P. Snyder, Integrative Personal Omics Pro les During Periods of Weight Gain and Loss, Cell System, 228; 6(2):157-170.e8, https://doi.org/10.1016/j.cels.2017.12.013, (2018) (Press coverage by NYtimes).
7. Asan Agibetov, Ernesto Jim enez-Ruiz, Marta Ondr esik, Alessandro Solimando, Imon Banerjee, Giovanna Guerrini, Chiara E. Catalano, Joaquim M. Oliveira, Giuseppe Patan e, Rui L. Reis, Michela Spagnuolo, Supporting Shared Hypothesis Testing in the Biomedical Domain, Journal of Biomedical Semantics, 9(1):9, https://doi.org/10.1186/s13326-018-0177-x, (2018).
8. Anupama Gupta, Imon Banerjee, Daniel L. Rubin, Automatic Information Extraction from Unstructured Mammography Reports Using Distributed Semantics, Journal of Biomedical Informatics Association, 78:78-86, https://doi.org/10.1016/j.jbi.2017.12.016, (2018).

2017

1. Imon Banerjee, Matthew C. Chen, Matthew P. Lungren, Daniel L. Rubin, Radiology Report Annotation using Intelligent Word Embeddings: Applied to Multi-institutional Chest CT Cohort, Journal of Biomedical Informatics Association, 77: 11-20, doi: https://doi.org/10.1016/j.jbi.2017.11.01, (2017) .
2. Imon Banerjee, Sadhika Malladi, Daniela Lee, Adrien Depeursinge, Melinda Telli, Ja Lipson, Dan Golden, and Daniel L. Rubin, Predicting Treatment Response in Triple Negative Breast Cancer from Quantitative Image Analysis in Perfusion MRI”, Journal of Medical Imaging, 5(1): 011008, https://doi.org/10.1117/1.JMI.5.1.011008, (2017).
3. Imon Banerjee, Christopher F. Beaulieu, Daniel L. Rubin,\Computerized Prediction of Radiological Observations Based on Quantitative Feature Analysis: Initial Experience in Liver Lesions”, Journal of Digital Imaging, 30(4):506-518, https://doi.org/10.1007/s10278-017-9987-0, (2017).
4. Imon Banerjee, Alexis Crawley, Mythili Bhethanabotla, Heike E Daldrup-Link, Daniel L. Rubin, Transfer Learning on Fused Multiparametric MR Images for Classifying Histopathological Subtypes of Rhabdomyosarcoma, Computerized Medical Imaging and Graphics, 65:167-175, https://doi.org/10.1016/j.compmedimag.2017.05.002, (2017).
5. Imon Banerjee, Arindam Sanyal, Statistical estimator for simultaneous noise and mismatch suppression in SAR ADC”, Electronics Letter, 53(12):773 – 775, https://doi.org/10.1049/el.2017.0928, (2017).
6. Imon Banerjee, Giuseppe Patan e, Michela Spagnuolo “Combination of Visual and Symbolic Knowledge: A Survey in Anatomy”, Computers in Biology and Medicine, 80:148-157, https://doi.org/10.1016/j.compbiomed.2016.11.018, (2017).

2016 and older

1. Imon Banerjee, Asan Agibetov, Chiara Eva Catalano, Giuseppe Patan e, Michela Spagnuolo “Semantics-driven Annotation of Patient-Specific 3D Data: A Step to Assist Diagnosis and Treatment of Rheumatoid Arthritis”, The Visual Computing journal, 32(10):1337-1349 https://doi.org/10.1007/s00371-016-1226-z, (2016).
2. Imon Banerjee, Chiara Eva Catalano, Giuseppe Patan e, Michela Spagnuolo “Semantic annotation of 3D anatomical models to support diagnosis and follow-up analysis of musculoskeletal pathologies”, International Journal of Computer Assisted Radiology and Surgery, 11(5):707-720, https://doi.org/10.1007/s11548-015-1327-6, (2015).
3. Abhinandan Dutta, Imon Banerjee “Priority based mathematical modeling for Grid computing environment”, Undergraduate Academic Research Journal (UARJ), 1(1): 112 – 117, 2012. ISSN : 2278 1129, (2012).

2022 – 2023

Ramasamy, Gokul, Bhavik N. Patel, and Imon Banerjee. “Anomaly Detection using Cascade Variational Autoencoder Coupled with Zero Shot Learning.” In Medical Imaging with Deep Learning, short paper track. 2023.

Correa-Medero, Ramon L., Bhavik Patel, and Imon Banerjee. “Adversarial Debiasing techniques towards ‘fair’skin lesion classification.” In 2023 11th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 1-4. IEEE, 2023.

Tariq, Amara, Siyi Tang, Hifza Sakhi, Leo Anthony Celi, Janice M. Newsome, Daniel Rubin, Hari Trivedi, Judy Gichoya, Bhavik Patel, and Imon Banerjee. “Graph-Based Fusion of Imaging and Non-Imaging Data for Disease Trajectory Prediction.” In 2023 11th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 1-4. IEEE, 2023.

Correra, Ramon, Jiwoong Jason Jeong, Bhavik Patel, Hari Trivedi, Judy W. Gichoya, and Imon Banerjee. “A robust two-step adversarial debiasing with partial learning: medical image case-studies.” In Medical Imaging 2023: Imaging Informatics for Healthcare, Research, and Applications, vol. 12469, pp. 31-38. SPIE, 2023.

Medero, Ramon Correa, Daniel Salevitz, Jiwoong Jeong, Bhavik Patel, Imon Banerjee, and Haidar Abdul-Muhsin. “MP09-07 DEVELOPING AN ARTIFICIAL INTELLIGENCE MODEL TO PREDICT DIFFERENTIAL RENAL FUNCTION USING CONTRAST-ENHANCED CT SCANS.” The Journal of Urology 209, no. Supplement 4 (2023): e106.

Guo, Xiaoyuan, Jiali Duan, Saptarshi Purkayastha, Hari Trivedi, Judy Wawira Gichoya, and Imon Banerjee. “OSCARS: An Outlier-Sensitive Content-Based Radiography Retrieval System.” arXiv preprint arXiv:2204.03074 (2022). ICMR ’22: Proceedings of the 2022 International Conference on Multimedia Retrieval

Santos, Thiago, Amara Tariq, Susmita Das, Kavyasree Vayalpati, Geoffrey H. Smith, Hari Trivedi, and Imon Banerjee. “PathologyBERT–Pre-trained Vs. A New Transformer Language Model for Pathology Domain.” arXiv preprint arXiv:2205.06885 (2022). AMIA Annual Symposium 2023.

Chao, Chieh Ju, James Shuyue Li, Jiwoong Jason Jeong, Timothy Barry, Amith R. Seri, Hana Neuman, Megan Campany et al. “ECHOCARDIOGRAPHY-BASED CONVOLUTIONAL NEURAL NETWORK ACCURATELY DIFFERENTIATES ETIOLOGIES OF INCREASED LEFT VENTRICULAR WALL THICKNESS.” Journal of the American College of Cardiology79, no. 9_Supplement (2022): 1195-1195.

2021

1. Amara Tariq, Hari Trivedi, Judy Wawira Gichoya, Imon Banerjee, “Patient-specific COVID-19 Resource Utilization Prediction Using Fusion AI model”, RSNA, 2021. (Aunt Minnie Roodies Winner)
2. Correa, Ramon, Jiwoong Jason Jeong, Bhavik Patel, Hari Trivedi, Judy W. Gichoya, and Imon Banerjee. Two-step adversarial debiasing with partial learning medical image casestudies. arXiv:2111.08711 (2021). AAAI 2022 Workshop: Trustworthy AI for Healthcare.
3. Thiago Santos, Omar N. Kallas, Janice Newsome, Daniel Rubin, Judy Wawira Gichoya, Imon Banerjee, A Fusion NLP Model for the Inference of Standardized Thyroid Nodule Malignancy Scores from Radiology Report Text”, AMIA Annual Symposium 2021.
4. Chandrasekaran, Sanjeev Tannirkulam, Imon Banerjee, and Arindam Sanyal. 7.5 nJ/inference CMOS Echo State Network for Coronary Heart Disease prediction.” In ESSDERC 2021-IEEE 51st European Solid-State Device Research Conference (ESSDERC), pp. 103-106. IEEE, 2021.
5. Sadasivuni, Sudarsan, Sumukh Prashant Bhanushali, Sai Srinivasa Singamsetti, Imon Banerjee, and Arindam Sanyal. Multi-Task Learning Mixed-Signal Classifirer for Detectionvof Atrial Fibrillation and Sepsis.” In 2021 IEEE Biomedical Circuits and Systems Conferencen(BioCAS), pp. 1-4. IEEE, 2021.
6. Tariq, Amara, Leo Celi, Janice Newsome, Saptarchi Purkayastha, Neal Bhatia, Faisal Merchant, Hari Trivedi, Judy Gichoya, and Imon Banerjee. “PATIENT-SPECIFIC COVID-19 RESOURCE UTILIZATION PREDICTION USING FUSION AI MODEL.” Journal of the American College of Cardiology 77, no. 18_Supplement_1 (2021): 3194-3194.
7. Zhou, Yuyin, Shih-Cheng Huang, Jason Alan Fries, Alaa Youssef, Timothy Amrhein, Marcello Kendrew Chang, Imon Banerjee et al. “RadFusion: Benchmarking Performance and Fairness for Multi-Modal Pulmonary Embolism Detection from CT and EMR.” (2021).
8. Sadasivuni, Sudarsan, Rahul Chowdhury, Vinay Elkoori Ghantala Karnam, Imon Banerjee, and Arindam Sanyal. “Recurrent Neural Network Circuit for Automated Detection of Atrial Fibrillation from Raw ECG.” In 2021 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1-5. IEEE, 2021.
9. Amara Tariq, Leo Anthony Celi, Janice M. Newsome, Saptarshi Purkayastha, Neal Kumar Bhatia, Faisal Merchant, Hari Trivedi, Judy Wawira Gichoya, Imon Banerjee,”Patient-specific covid-19 resource utilization prediction using fusion AI model”, ACC’s 70th Annual Scientific Session, May 15-17, 2021, in Atlanta, Georgia.
10. Dipam Paul, Alankrita Tewari , Imon Banerjee, Super Learner Model to Detect Abnormalities – OCT and blood smear imaging case studies, AAAI 2021 Workshop on Trustworthy AI for Healthcare.
11. Dipam Paul, Alankrita Tewari , Imon Banerjee, “Boosting Classification Accuracy of Fertile Sperm Cell Images leveraging cDCGAN”, ICLR SEDL 2021.
12. Eric J. Clayton, Imon Banerjee, Patrick J. Ward, Maggie D Howell, Beth Lohmueller, Sunita Pierson, Rachel Hall, Peter B Harrison, David Michael Waterhouse, “A natural language processing tool for automatic identification of new disease and disease progression: Parsing text in multi-institutional radiology reports to facilitate clinical trial eligibility screening”, ASCO Annual Meeting. 2021.

2020

1. Amara Tariq,Marly van Assen, Carlo N De Cecco, Imon Banerjee, “Computing CAD-RADS Score using free-text from Coronary CT Angiography Reports”, Conference in Machine Intelligence in Medical Imaging, CMIMI 2020.
2. Xiaoyuan Guo, Judy Gichoya, Hari Trivedi, Imon Banerjee, “Deeper Thinner UNet (DT-UNet) for Fine Vessel Segmentation of Breast Arterial Calcifications (BAC)”, Conference in Machine Intelligence in Medical Imaging, CMIMI 2020.
3. Yasmin H Karimi, Douglas W. Blayney, Allison W. Kurian, Daniel Rubin, Imon Banerjee, Development and validation of natural language processing (NLP) algorithm for detection of distant versus local breast cancer recurrence and metastatic site, ASCO Annual meeting 2020.
4. Scott J. Lee, Brent D. Weinberg, Ashwani Gore, Imon Banerjee, A Novel Natural Language Processing Method for Inferring BT-RADS Scores from Unstructured MRI Reports in Patients with Primary Brain Tumors, SIIM Annual Meeting 2020.
5. Hari Trivedi, Imon Banerjee, Machine Learning for Automatic Neuro MR Study Protocoling, SIIM Annual Meeting 2020.
6. Hari Trivedi, Imon Banerjee, Automatic Vetting of Appropriateness of Radiology Studies using Machine Learning, SIIM Annual Meeting 2020.
7. Imon Banerjee, Selen Bozkurt, Jennifer Lee Caswell-Jin, Allison W. Kurian, Daniel L. Rubin, Detection of Metastatic Recurrence Timeline from Clinical Notes, AMIA informatics 2020 (accepted as podium abstract).
8. Josh Sanyal, Imon Banerjee, Lewis Hahn, Daniel Rubin, An Automated Two-step Pipeline for Aggressive Prostate Lesion Detection from Multi-modal MR Sequence, AMIA informatics 2020 (accepted as regular paper).
9. Jiaming Zeng, Imon Banerjee, Michael Francis Gensheimer, Daniel Rubin, Cancer Treatment Classification with Electronic Medical Health Records, AAAI Conference on Artificial Intelligence (AAAI-20)

2019

1. Imon Banerjee, Miji Sofela, Timothy Amrhein, MD8; Daniel L. Rubin, Roham Zamanian, Matthew P. Lungren, Prediction of Imaging Outcomes from Electronic Health Records: Pulmonary Embolism Case-Study, AMIA Annual Symposium, 2019.
2. Ron C. Li, Imon Banerjee, Daniel Rubin, Jonathan H. Chen “Detecting unanticipated actions downstream from clinical decision support: a data mining approach”, AMIA Annual Symposium, 2019.
3. Terry Desser, Imon Banerjee, Haliey Choi, Roderick King, Daniel Rubin, Identifying missed hepatocellular carcinomas in an ultrasound screening and surveillance program using artificial intelligence, European Congress of Radiology (ECR), 2019.
4. Josh Sanyal, Imon Banerjee, Daniel Rubin, Registration Boost Performance of Aggressive Prostate Cancer Diagnosis,American Medical Informatics Association (AMIA) Informatics Summit, 2019.

2018

1. Imon Banerjee, Hailey H. Choi, Terry Desser, Daniel L. Rubin, A Scalable Machine Learning Approach for Inferring Probabilistic US-LI-RADS Categorization”, American Medical Informatics Association (AMIA) Annual Symposium 2018, https://arxiv.org/abs/1806.07346.
2. Imon Banerjee, Hailey H. Choi, Daniel L. Rubin, Terry Desser, Application of Machine Learning to Infer Ultrasound LI-RADS Categories across Multi-Institutional Radiology Reports”, 104th Scienti c Assembly and Annual Meeting of the Radiological Society of North America (RSNA) 2018.
3. Hailey H. Choi, Imon Banerjee, Aya Kamaya, Daniel L. Rubin, Terry Desser, Machine Learning for rapid assessment of outcomes of an ultrasound screening and surveillance program in patients at risk for hepatocellular carcinoma”, 104th Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA) 2018, (selected for press review).
4. Imon Banerjee, Hailey H. Choi, Terry Desser, Daniel L. Rubin, A distributional semantics model for automatically assigning ACR Ultrasound LI-RADS categories across multi-template and multi-institutional radiology reports”, 3rd Annual Scientific Conference on Machine Intelligence in Medical Imaging (C-MIMI) of the Society for Imaging Informatics in Medicine (SIIM), PDF link, 2018.
5. Selen Bozkurt, Emel Alkim, Imon Banerjee, Daniel L. Rubin, “Automated Detection of Lesion Measurements and Relationships in Radiology Reports Using a Hybrid Natural Language Processing Algorithm”, 3rd Annual Scientific Conference on Machine Intelligence in Medical Imaging (C-MIMI) of the Society for Imaging Informatics in Medicine (SIIM), PDF link, 2018.
6. Imon Banerjee, Michael Francis Gensheimer, J. Douglas Wood, Solomon Henry, Daniel T. Chang, Daniel L. Rubin, Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) Utilizing Free-Text Clinical Narratives”, American Medical Informatics Association (AMIA) Informatics Summit, PDF link, 2018 (presented as podium abstract).
7. Kevin Li, Imon Banerjee, Christopher J. Magnani, Tina Seto, Douglas W. Blayney, James D. Brooks, Tina Hernandez-Boussard, Practice-based evidence for factors associated with urinary incontinence following prostate cancer care, Genitourinary Cancers Symposium, 2018.

2017

1. Imon Banerjee, Sriraman Madhavan, Roger Eric Goldman, Daniel L. Rubin, Intelligent Word Embeddings of Free-Text Radiology Reports”, American Medical Informatics Association (AMIA) Annual Symposium, PDF link, 2017.
2. Paroma Varma, Bryan He, Payal Bajaj, Imon Banerjee, Nishith Khandwala, Daniel L. Rubin, Christopher R e, Inferring Generative Model Structure with Static Analysis, Advances in Neural Information Processing Systems (NIPS), https://papers.nips.cc/paper/6628-inferring– generative-model-structure-with-static-analysis.pdf, 2017.
3. Stephen R. Vossler, Imon Banerjee, Bao H. Do, Daniel L. Rubin, Christopher F. Beaulieu, Semantic Labeling of Musculoskeletal Radiographs Using Deep Learning, The Radiological Society of North America (RSNA) annual meeting, 2017 (Trainee award).
4. Imon Banerjee , “Integration of Shape Analysis and Knowledge Techniques for the Semantic Annotation of Patient-Speci c 3D Data” Journal of Medical Physics, online Ph.D. thesis abstract, 2017.
5. Imon Banerjee , Sriraman Madhavan, Roger Eric Goldman, Daniel L. Rubin, “Annotation of Medical Data using Intelligent Embedding of Free-text Clinical Notes” Big Data in Biomedicine Conference, Stanford, 2017.

2016

1. Imon Banerjee , Lewis Hahn, Geo rey Sonn, Richard Fan, Daniel L. Rubin,\Computerized Multiparametric MR image Analysis for Prostate Cancer Aggressiveness-Assessment.” NIPS 2016 Workshop on Machine Learning for Health, https://arxiv.org/abs/1612.00408, 2016.

2015

1. Imon Banerjee , Hamid Laga, Guiseppe Patan e, Sebastian Kurtek, Anuj Srivastava, Michela Spagnuolo, “Generation of 3D Canonical Anatomical Models: An Experience on Carpal Bones”, New Trends in Image Analysis and Processing – ICIAP 2015, https://doi.org/10.1007/978-3-319-23222-5 21, Springer 2015.
2. Imon Banerjee , Asan Agibetov, Chiara Eva Catalano, Giuseppe Patan e, Michela Spagnuolo, “Semantic Annotation of Patient-Speci c 3D Anatomical Models”, IEEE Proceedings of the International Conference on Cyberworlds, IEEE 2015.
3. Imon Banerjee , Giuseppe Patan e, Michela Spagnuolo, “SemAnatomy3D: Annotation of Patient-Speci c Anatomy, EuroGraphics Digital Library, Stag 2015, http://dx.doi.org/10.2312/stag.20151292.
4. Imon Banerjee , Chiara Eva Catalano, Giuseppe Patan e, Michela Spagnuolo, “Semantic annotation of 3D anatomical models to support diagnosis and follow-up analysis of musculoskeletal pathologies, Computer Assisted Radiology and Surgery (CARS), 2015.

2014 and older

1. Imon Banerjee , Marios Pitikakis, Knowledge management in medicine: a framework to organize, browse and search medical data, International Conference on Health Informatics, Healthinf 2014.
2. Imon Banerjee , Animesh Dutta, Shrutilipi Bhattacharjee, “Formal Design of Teleteaching Interactivity”, ITC ’10 Proceedings of the 2010 International Conference on Recent Trends in Information, Telecommunication and Computing, 2010.
3. Animesh Dutta, Imon Banerjee , Shrutilipi Bhattacharya, Ranjan Dasgupta, Swapan Bhattachary ,\Framework for Domain Analysis of Teleteaching System: A Semiformal Approach”, Proceedings of the 2010 International Conference on Software Engineering Research & Practice, SERP 2010.
4. Shrutilipi Bhattacharjee, Imon Banerjee , Animesh Datta, “An Ontology Based Framework for Domain Analysis of Interactive System.”,Contemporary Computing – Third International Conference, IC3 2010.
5. Imon Banerjee , Shrutilipi Bhattacharjee, “Design of Tele-healthcare Interactivity: A Semi Formal Approach”, 2nd Annual International Conference IEMCON 2012.

1. Imon Banerjee , Chiara Eva Catalano, Giuseppe Patan e, Michela Spagnuolo, “Semantic annotation of 3D patient-specific models.”, Technical Report. 2015 IMATI, Genova.

2. Imon Banerjee , Chiara Eva Catalano, Giuseppe Patan e, Francesco Robbiano, Michela Spagnuolo, “Survey on medical ontologies.”, Technical Report.2012 IMATI, Genova.