39 citations · 59 across the 11 of their papers we have counts for
11 papers
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…
Improving Text Style Transfer using Masked Diffusion Language Models with Inference-time Scaling
Tejomay Kishor Padole, Suyash P Awate, Pushpak Bhattacharyya
Masked diffusion language models (MDMs) have recently gained traction as a viable generative framework for natural language. This can be attributed to its scalability and ease of t…
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
Karim Lekadir, Aasa Feragen, Abdul Joseph Fofanah +117
Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. I…
Uncertainty-aware GAN with Adaptive Loss for Robust MRI Image Enhancement
Uddeshya Upadhyay, Viswanath P. Sudarshan, Suyash P. Awate
Image-to-image translation is an ill-posed problem as unique one-to-one mapping may not exist between the source and target images. Learning-based methods proposed in this context…
Towards Lower-Dose PET using Physics-Based Uncertainty-Aware Multimodal Learning with Robustness to Out-of-Distribution Data
Viswanath P. Sudarshan, Uddeshya Upadhyay, Gary F. Egan +2
Radiation exposure in positron emission tomography (PET) imaging limits its usage in the studies of radiation-sensitive populations, e.g., pregnant women, children, and adults that…
A Mixed-Supervision Multilevel GAN Framework for Image Quality Enhancement
Uddeshya Upadhyay, Suyash Awate
Deep neural networks for image quality enhancement typically need large quantities of highly-curated training data comprising pairs of low-quality images and their corresponding hi…
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…
Sparse Kernel PCA for Outlier Detection
Rudrajit Das, Aditya Golatkar, Suyash P. Awate
In this paper, we propose a new method to perform Sparse Kernel Principal Component Analysis (SKPCA) and also mathematically analyze the validity of SKPCA. We formulate SKPCA as a…
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…