Our team source code

We propose an integrative approach, built upon physics based simulations, nanoscopy algorithms, and shallow residual attention network to make it possible for the first time to analysis sub-resolution motion patterns in vesicles that may also be of sub-resolution diameter.

For more details - CVPR 2020 paper . Sekh, A. A., Opstad, I. S., Birgisdottir, A. B., Myrmel, T., Ahluwalia, B. S., Agarwal, K., & Prasad, D. K. (2020). Learning nanoscale motion patterns of vesicles in living cells. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 14014-14023). 

Neural network based country wise risk prediction of COVID-19

For more details - Pal, R., Sekh, A. A., Kar, S., & Prasad, D. K. (2020). Neural network based country wise risk prediction of COVID-19. Applied Sciences, 10(18), 6448. Link

 A custom deep learning architecture, LightOCT, is proposed for the classification of OCT images into diagnostically relevant classes. LightOCT is a convolutional neural network with only two convolutional layers and a fully connected layer, but it is shown to provide excellent training and test results for diverse OCT image datasets. We show that LightOCT provides 98.9% accuracy in classifying 44 normal and 44 malignant (invasive ductal carcinoma) breast tissue volumetric OCT images. 

A.Butola, D. K. Prasad, A. Ahmad, V. Dubey, D. Qaiser, A. Srivastava, P. Senthilkumaran, B. S. Ahluwalia, and D. S. Mehta. "Deep learning architecture LightOCT for diagnostic decision support using optical coherence tomography images of biological samples.", Biomedical Optics Express, 2020. (Source code and pretrained model)