39 citations · 59 across the 11 of their papers we have counts for
5 papers · 1 filter
Learning a Sampling-Free Variational DNN Plugin from Tiny Training Sets to Refine OOD Segmentation With Uncertainty Estimation
Jimut B. Pal, Suyash P. Awate
Deep neural networks (DNNs) frequently fail to generalize to out-of-distribution (OOD) medical images because of variations in scanners and acquisition protocols. Retraining DNN mo…
Single Test Image-Based Automated Machine Learning System for Distinguishing between Trait and Diseased Blood Samples
Sahar A. Nasser, Debjani Paul, Suyash P. Awate
We introduce a machine learning-based method for fully automated diagnosis of sickle cell disease of poor-quality unstained images of a mobile microscope. Our method is capable of…
Robust Super-Resolution GAN, with Manifold-based and Perception Loss
Uddeshya Upadhyay, Suyash P. Awate
Super-resolution using deep neural networks typically relies on highly curated training sets that are often unavailable in clinical deployment scenarios. Using loss functions that…
Annotation-cost Minimization for Medical Image Segmentation using Suggestive Mixed Supervision Fully Convolutional Networks
Yash Bhalgat, Meet Shah, Suyash Awate
For medical image segmentation, most fully convolutional networks (FCNs) need strong supervision through a large sample of high-quality dense segmentations, which is taxing in term…
Hierarchical Graphical Models for Multigroup Shape Analysis using Expectation Maximization with Sampling in Kendall's Shape Space
Yen-Yun Yu, P. Thomas Fletcher, Suyash P. Awate
This paper proposes a novel framework for multi-group shape analysis relying on a hierarchical graphical statistical model on shapes within a population.The framework represents in…