Publications:
[Google Scholar] [GitHub ]
Year 2025:
[J3]. Manna, A., Sista, R., Sheet, D., 2025. Deep neural hashing for content-based medical image retrieval: A survey, Computers in Biology and Medicine, 196, p.110547. [Paper]
[J2]. Manna, A., Dewan, D. and Sheet, D., 2025. Structured hashing with deep learning for modality, organ, and disease content sensitive medical image retrieval. Scientific Reports, 15(1), p.8912. [Paper] [Code]
[J1]. Manna, A., Sathish, R., Sethuraman, R. and Sheet, D., 2025. OPHash: learning of organ and pathology context-sensitive hashing for medical image retrieval. Journal of Medical Imaging, 12(1), p.017503-017503. [Paper] [Code]
[C6]. Sun, Lei, et al. The tenth ntire 2025 image denoising challenge report. In Proceedings of the Computer Vision and Pattern Recognition Conference. pp. 1342-1369. [Paper] [Code]
Year 2024:
[C5]. Manna, A. and Sheet, D. Learning Neural Networks for Multi-label Medical Image Retrieval Using Hamming Distance Fabricated with Jaccard Similarity Coefficient. In International Conference on Pattern Recognition (pp.251-266). [Paper] [Code]
[C4]. Dewan, D., Manna, A., Srivastava, A., Borthakur, A. and Sheet, D., ”MeDiANet: A Lightweight Network for Large-scale Multi-disease Classification of Multi-modal Medical Images Using Dilated Convolution and Attention Network.” In ICPR, pp. 170-184. Springer, Cham.
[C3]. Manna, A. and Sheet, D. . Research Reproducibility Paper: learning neural networks for multi-label medical image retrieval using Hamming distance fabricated with Jaccard similarity coefficient. In Reproducible Research in Pattern Recognition.
[C2]. Dewan, D., Manna, A., Mohanty, D., and Sheet, D., "Companion Paper: MeDiANet Implementation and Reproducibility Details.", In Reproducible Research in Pattern Recognition.
Year 2023:
[C1]. Borthakur, A., Kasliwal, A., Manna, A., Borthakur, A., Kasliwal, A., Manna, A., Dewan, D. and Sheet, D., 2024, September. FedERA: Framework for Federated Learning with Diversified Edge Resource Allocation. IEEE International Conference on Federated Learning Technologies and Applications (FLTA) (pp. 47-54). [Paper] [Code]