Research

Research Profiles: DBLP, Google Scholar, IRINS, ORCID

Journal Publications

1. Jayant Mahawar & Angshuman Paul. Generalizable diagnosis of chest radiographs through attention-guided decomposition of images utilizing self-consistency loss. In Computers in Biology and Medicine (2024), 180, 108922.

2. Nirbhay Sharma, Gautam Kumar, & Angshuman Paul. An Extremely Lightweight CNN Model For the Diagnosis of Chest Radiographs in Resource-constrained Environments. In Medical Physics (2023), 50 (12): 7568-78.

3. Manasi Mukherjee, Angshuman Paul, & Mitali Mukerji. Biotic Assessment of Crowdsourced Data Defines Four Ecoregions in Thar: A Novel Approach for Community Engagement in Conservation. In Global Ecology and Conservation (2023), p.e02559.

4. Saikat Sarkar, Spandan Basu, Angshuman Paul, & Dipti Prasad Mukherjee. ViViD: View Prediction of Online Video through Deep Neural Network based Analysis of Subjective Video Attributes. In IEEE Transactions on Broadcasting. 69.1 (2023): 191-200. 

5. Lucian G. Eftimie, Remus R. Glogojeanu, Tejaswee A, Pavel Gheorghita, Stefan G. Stanciu, Augustin Chirila, George A. Stanciu, Angshuman Paul, & Radu Hristu. Differential Diagnosis of Thyroid Nodule Capsules Using Random Forest Guided Selection of Image Features. In Scientific Reports, 12 (1) (2022), 21636.

6. Aratrik Chattopadhyay, Angshuman Paul, & Dipti Prasad Mukherjee. Detail Preserving Conditional Random Field as 2-D RNN for Gland Segmentation in Histology Images. In Pattern Recognition Letters, 159 (2022), 38-45 (DOI: 10.1016/j.patrec.2022.05.001 ). 

7. Angshuman Paul, Thomas C. Shen, Sungwon Lee, Niranjan Balachandar, Yifan Peng, Zhiyong Lu, & Ronald M. Summers (2021). Generalized Zero-shot Chest X-ray Diagnosis through Trait-Guided Multi-view Semantic Embedding with Self-training. In IEEE Transactions on Medical Imaging, 40.10 (2021): 2642-2655. (DOI: 10.1109/TMI.2021.3054817).  

8. Angshuman Paul, Yu-Xing Tang, Thomas C. Shen, & Ronald M. Summers (2021). Discriminative Ensemble Learning for Few-shot Chest X-ray Diagnosis. In Medical Image Analysis, 68 (2021): 101911 (DOI: 10.1016/j.media.2020.101911). 

9. Bikash Santra, Angshuman Paul, & Dipti Prasad Mukherjee (2020). Deterministic Dropout for Deep Neural Networks Using Composite Random Forest. In Pattern Recognition Letters, 131 (2020): 205-212.

10. Angshuman Paul, & Dipti Prasad Mukherjee (2019). Reinforced Quasi-random Forest. In Pattern Recognition, 94 (2019): 13-24.

11. Angshuman Paul, Dipti Prasad Mukherjee, & Scott T. Acton (2018). Speckle Removal Using Diffusion Potential for Optical Coherence Tomography Images. In IEEE Journal of Biomedical and Health Informatics, 23.1 (2018): 264-272.

12. Angshuman Paul, Dipti Prasad Mukherjee, Prasun Das, Abhinandan Gangopadhyay, Appa Rao Chintha, & Saurabh Kundu (2018). Improved Random Forest for Classification. In IEEE Transactions on Image Processing, 27.8 (2018): 4012-4024.

13. Angshuman Paul, Abhinandan Gangopadhyay, Appa Rao Chintha, Dipti Prasad Mukherjee, Prasun Das, & Saurabh Kundu (2018). Calculation of Phase Fraction in Steel Microstructure Images Using Random Forest Classifier. In IET Image Processing, 12.8 (2018): 1370-1377.

14. Angshuman Paul, & Dipti Prasad Mukherjee (2015). Mitosis Detection for Invasive Breast Cancer Grading in Histopathological Images. In IEEE Transactions on Image Processing, 24.11 (2015): 4041-4054.

Conference Publications

1. Dattatreyo Roy & Angshuman Paul. Anomaly-focused Single Image Super-resolution with Artifact Removal for Chest X-rays using Distribution-aware Diffusion Model. Medical Imaging with Deep Learning. 2024 (Accepted).

2. Shilajit Banerjee & Angshuman Paul (2024). An Ensemble of Well-trained Students Can Perform Almost As Good As a Teacher for Chest X-ray Diagnosis. In IEEE International Symposium on Biomedical Imaging, 2024 (Accepted).

3. Jayant Mahawar, Bhargab Chattopadhyay  & Angshuman Paul (2024). Label-guided Coreset Generation for Computationally Efficient Chest X-ray Diagnosis. In IEEE International Symposium on Biomedical Imaging, 2024 (Accepted).

4. Nitya A. Shah, Jinal Suthar, Tejaswee A, Adrian Enache, Lucian G. Eftimie, Radu Hristu, & Angshuman Paul (2024). Deep Learning-based Diagnosis of Thyroid Tumors using Histopathology Images from Thyroid Nodule Capsule.  In Medical Imaging 2024: Computer-Aided Diagnosis. Vol. 12927. SPIE, 2024.

5. Krishna Thoriya, Preeti Mutreja, Sumit Kalra, & Angshuman Paul (2023). Multi-Task Learning for Few-Shot Differential Diagnosis of Breast Cancer Histopathology Images. In Workshop on Medical Image Learning with Limited and Noisy Data 2023 @ MICCAI 2023 (pp. 202-210).

6. Vamshi Vardhan Yadagiri, Sekhar Reddy, & Angshuman Paul (2023). Anomaly Guided Generalizable Super-Resolution of Chest X-Ray Images using Multi-Level Information Rendering. In Workshop on Deep Generative Models for Medical Image Computing and Computer Assisted Intervention (DGM4MICCAI) @ MICCAI 2023 (pp. 77-85).

7. M K Laksath Adityan, Himanchal Sharma, & Angshuman Paul (2023). Segmentation and Classification-based Diagnosis of Tumors from Breast Ultrasound Images using Multibranch U-Net. In IEEE International Conference on Image Processing (ICIP), pp. 2505-2509. IEEE, 2023. 

8. Kshitiz, Garvit Garg, & Angshuman Paul (2023). Few-shot Diagnosis of Chest x rays Using an Ensemble of Random Discriminative Subspaces. In ICLR Workshop on Machine Learning & Global Health, 2023.

9. Keshava Chowdari Dabbara, Radhasyam Nunna, Anabik Pal, & Angshuman Paul (2023). Federated learning using multi-institutional data for generalizable chest X-ray diagnosis. In Medical Imaging 2023: Computer-Aided Diagnosis, 2023 (Vol. 12465, pp. 124650H). International Society for Optics and Photonics.

10. Jared Gregory Frazier, Tejas Sudharshan Mathai, Jianfei Liu, Angshuman Paul, & Ronald M. Summers (2023).  3D universal lesion detection and tagging in CT with self-training. In Medical Imaging 2023: Computer-Aided Diagnosis, 2023  (Vol. 12465, pp. 765-772). International Society for Optics and Photonics.

11. Alexander Te-Wei Shieh, Tejas Sudharshan Mathai, Jianfei Liu, Angshuman Paul, & Ronald M. Summers. Correcting class imbalances with self-training for improved universal lesion detection and tagging. In Medical Imaging 2023: Computer-Aided Diagnosis, 2023 (Vol. 12465, pp. 224-235). International Society for Optics and Photonics.

12. Varun Naga, Tejas Sudharshan Mathai, Angshuman Paul, & Ronald M. Summers (2022). Universal Lesion Detection and Classification using Limited Data and Weakly-Supervised Self-Training. In Workshop on Medical Image Learning with Limited and Noisy Data 2022 @ MICCAI 2022 (pp. 55-64) @ MICCAI, Springer.

13. Angshuman Paul, Thomas C. Shen, Yifan Peng, Zhiyong Lu, & Ronald M. Summers (2021, April). Learning Few-shot Chest X-ray Diagnosis Using Images from the Published Scientific Literature. In International Symposium on Biomedical Imaging (ISBI), 2021 (pp. 344-348). IEEE. 

14. Angshuman Paul, Thomas C. Shen, Niranjan Balachandar, Yuxing Tang, Yifan Peng, Zhiyong Lu, & Ronald M. Summers (2020). COMe-SEE: Cross-Modality Semantic Embedding Ensemble for Generalized Zero-Shot Diagnosis of Chest Radiographs. In Interpretable and Annotation-Efficient Learning for Medical Image Computing @ MICCAI, 2020 (pp. 103-111), Springer.

15. Angshuman Paul, Yu-Xing Tang, & Ronald M. Summers (2020, February). Fast Few-shot Transfer Learning for Disease Identification from Chest x-ray Images using Autoencoder Ensemble. In Medical Imaging 2020: Computer-Aided Diagnosis, 2020 (vol. 11314, p. 1131407). International Society for Optics and Photonics.

16. Sai A. Sriram, Angshuman Paul, Yingying Zhu, Veit Sandfort, Perry J. Pickhardt, & Ronald M. Summers (2020, February). Multilevel UNet for Pancreas Segmentation from Non-contrast CT Scans through Domain Adaptation. In Medical Imaging 2020: Computer-Aided Diagnosis, 2020 (Vol. 11314, p. 113140K). International Society for Optics and Photonics.

17. Angshuman Paul, Dipti Prasad Mukherjee, & Scott T. Acton (2019). Shape Based Speckle Removal for Ultrasound Image Segmentation. (ICIP), 2019 (pp. 3586-3590). IEEE. 

18. Angshuman Paul, Angshul Majumdar, & Dipti Prasad Mukherjee (2018). Discriminative Autoencoder. In IEEE International Conference on Image Processing (ICIP), 2018 (pp. 3049-3053). IEEE.

19. Angshuman Paul, & Dipti Prasad Mukherjee (2016, December). Reinforced Random Forest. In Indian Conference on Computer Vision, Graphics and Image Processing, (ICVGIP) (pp. 1-8). ACM. (2016, December). (*Best Paper Award) 

20. Angshuman Paul, & Dipti Prasad Mukherjee (2016, September). Gland Segmentation from Histology Images Using Informative Morphological Scale Space. In IEEE International Conference on Image Processing (ICIP), 2016 (pp. 4121-4125). IEEE. 

21. Angshuman Paul, Anisha Dey, Dipti Prasad Mukherjee, Jayanthi Sivaswamy, & Vijaya Tourani (2015, October). Regenerative Random Forest with Automatic Feature Selection to Detect Mitosis in Histopathological Breast Cancer Images. In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2015, pp. 94-102. 

22. Angshuman Paul, & Dipti Prasad Mukherjee (2014, December). Enhanced Random Forest for Mitosis Detection. In Indian Conference on Computer Vision, Graphics and Image Processing, (ICVGIP) (pp. 1-8). ACM, 2014.

23. Ananda S. Chowdhury, Angshuman Paul, Filiz Bunyak, D. D. W. Cornelison, & K. Palaniappan (2012, September). Semi-automated Tracking of Muscle Satellite Cells in Brightfield Microscopy Video. In IEEE International Conference on Image Processing (ICIP), 2012 (pp. 2825-2828). 

24. Angshuman Paul, Nilotpal Bhattacharya, & Ananda S. Chowdhury (2012, December). Digit Recognition from Pressure Sensor Data Using Euler Number and Central Moments. In International Conference on Communications, Devices and Intelligent Systems (CODIS), 2012 (pp. 93-96). IEEE.