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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.
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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). https://nanoscalemotion.github.io/
Neural network based country wise risk prediction of COVID-19
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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. https://covid19prediction.github.io/
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.
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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: https://www.dropbox.com/s/nzfme6xn1k4dtkd/LighOCT.zip?dl=0