Saha, M., Chakraborty, C., 2018. "Her2Net: A Deep Framework for Semantic Segmentation and Classification of Cell Membranes and Nuclei in Breast Cancer Evaluation," in IEEE Transactions on Image Processing, vol. 27 PP. 2189-2200, no. 5, pp. 1-1. doi: 10.1109/TIP.2018.2795742
Saha, M., Chakraborty, C., and Racoceanu, D., 2018. "Efficient Deep Learning Model for Mitosis Detection using Breast Histopathology Images," Computerized Medical Imaging and Graphics, vol. 64 PP. 29-40, doi: https://doi.org/10.1016/j.compmedimag.2017.12.001
Saha, M., Chakraborty, C., Arun, I., Ahmed, R. and Chatterjee, S., 2017. “An Advanced Deep Learning Approach for Ki-67 Stained Hotspot Detection and Proliferation Rate Scoring for Prognostic Evaluation of Breast Cancer”. Scientific Reports-Nature, 7,doi: 10.1038/s41598-017-03405-5
Saha, M., Arun, I., Agarwal, S., Ahmed, R., Chatterjee, S. and Chakraborty, C., 2017. “Imprint cytology‐based breast malignancy screening: an efficient nuclei segmentation technique”. Journal of Microscopy, doi: 10.1111/jmi.12595
Saha, M., Arun, I., Basak, B., Agarwal, S., Ahmed, R., Chatterjee, S., Bhargava, R. and Chakraborty, C., 2016. “Quantitative microscopic evaluation of mucin areas and its percentage in mucinous carcinoma of the breast using tissue histological images”. Tissue and Cell, 48(3), pp.265-273, doi: https://doi.org/10.1016/j.tice.2016.02.005
Saha, M., Mukherjee, R. and Chakraborty, C., 2016. “Computer-aided diagnosis of breast cancer using cytological images: A systematic review”. Tissue and Cell, 48(5), pp.461-474, doi: https://doi.org/10.1016/j.tice.2016.07.006
Banerjee, S., Saha, M., Arun, I., Basak, B., Agarwal, S., Ahmed, R., Chatterjee, S., Mahanta, L. B., and Chakraborty, C., 2017. “Near-set based mucin segmentation in histopathology images for detecting mucinous carcinoma”. Journal of Medical Systems, doi: 10.1007/s10916-017-0792-6
Mukherjee, R., Saha, M., Routray, A. and Chakraborty, C., 2015. “Nanoscale Surface Characterization of Human Erythrocytes by Atomic Force Microscopy: A Critical Review”. IEEE transactions on NanoBioscience, 14(6), pp.625-633, doi: 10.1109/TNB.2015.2424674
Saha, M., Arun, I., and Chakraborty, C., 2017. HerNet: An Automated HER-2 Scoring Tool for Breast Cancer Screening using Deep Learning. 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'17)
Saha, M., Mukherjee, R. Arun, I, Chatterjee, S., and Chakraborty, C., 2016. Computer-aided detection of diagnostically relevant tubule region in H&E stained breast cancer, histopathology images, Journal of carcinogenesis 15, S33
Saha, M., Agarwal, S., Arun, I., Ahmed, R., Chatterjee, S., Mitra, P. and Chakraborty, C., 2015. Histogram based thresholding for automated nucleus segmentation using breast imprint cytology. In Advancements of Medical Electronics (pp. 49-57). Springer, New Delhi, doi: https://doi.org/10.1007/978-81-322-2256-9_5
Saha, M., Ray, A.K. and Basu, S.K., 2012.3D CA model of tumor-induced angiogenesis. International Conference on Modeling and Simulation of Diffusive Processes and Applications (ICMSDPA12), pp-177-181