Asha and Keshav Bhide Chair Professor
Computer Science and Engineering Department, Indian Institute of Technology (IIT) Bombay
Office: A-214, Kanwal Rekhi Building
Email: my-first-name @cse.iitb.ac.in
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Suyash P.
Awate Asha and Keshav Bhide Chair Professor Computer Science and Engineering Department, Indian Institute of Technology (IIT) Bombay Office: A-214, Kanwal Rekhi Building Email: my-first-name @cse.iitb.ac.in |
| Research | Publications | Teaching | Students | CV | Personal |
| Image Segmentation and Classification with
Kernel Methods and Deep Neural Networks |
| Jena R, Awate SP A Bayesian neural net to segment images with uncertainty estimates and good calibration Information Processing in Medical Imaging (IPMI) 2019, xx-xx, Springer LNCS xx (podium presentation, acceptance rate ~15%) |
| Shah M, Merchant SN, Awate SP MS-Net: Mixed-supervision fully-convolutional networks for full-resolution segmentation Medical Image Computing and Computer Assisted Intervention (MICCAI) 2018; 21(4):379-387 Springer LNCS 11073 (in top 10 papers nominated for MICCAI Young Scientist Award, podium presentation, acceptance rate 5%) |
| Shah M, Merchant SN, Awate SP Abnormality detection using deep neural networks with robust autoencoding and semi-supervision IEEE Int. Symposium on Biomedical Imaging (ISBI) 2018 (podium presentation, acceptance rate 17%) |
| Kumar N, Rajwade A, Chandran S, Awate SP Kernel generalized Gaussian and robust statistical learning for abnormality detection in medical images IEEE Int. Conf. Image Processing (ICIP) 2017, 4157-4161 (among top 10 finalists for Best Paper / Best Student Paper Award from 3000+ submissions, student travel award) |
| Kumar N, Rajwade A, Chandran S, Awate SP Kernel generalized-Gaussian mixture model for robust abnormality detection Medical Image Computing and Computer Assisted Intervention (MICCAI) 2017; 20(3):21-29 Springer LNCS 10435 |
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Shah M, Singha S,Awate SP Leaf classification using marginalized shape context and shape+texture dual-path deep convolutional neural network IEEE Int. Conf. Image Processing (ICIP) 2017, xx:xx-xx (podium presentation)  Our Dual-Path CNN. “BN + ReLU” batch normalization followed by ReLU activation; N number of classes.  Example Leaves Differing in Shape and Texture. Leaf images input to the CNN path (left path in Figure 1) primarily capturing shape.  Marginalized Shape Context for 3 leaves: the leaves on the left and the middle belong to the same species. |
| Related Works |