4 citations · 7 across the 5 of their papers we have counts for
6 papers
SetLexSem Challenge: Using Set Operations to Evaluate the Lexical and Semantic Robustness of Language Models
Bardiya Akhbari, Manish Gawali, Nicholas A. Dronen
Set theory is foundational to mathematics and, when sets are finite, to reasoning about the world. An intelligent system should perform set operations consistently, regardless of s…
Application of Federated Learning in Building a Robust COVID-19 Chest X-ray Classification Model
Amartya Bhattacharya, Manish Gawali, Jitesh Seth +1
While developing artificial intelligence (AI)-based algorithms to solve problems, the amount of data plays a pivotal role - large amount of data helps the researchers and engineers…
Vulnerability Due to Training Order in Split Learning
Harshit Madaan, Manish Gawali, Viraj Kulkarni +1
Split learning (SL) is a privacy-preserving distributed deep learning method used to train a collaborative model without the need for sharing of patient's raw data between clients.…
Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practice
Viraj Kulkarni, Manish Gawali, Amit Kharat
The use of machine learning to develop intelligent software tools for interpretation of radiology images has gained widespread attention in recent years. The development, deploymen…
Deep Learning Models for Calculation of Cardiothoracic Ratio from Chest Radiographs for Assisted Diagnosis of Cardiomegaly
Tanveer Gupte, Mrunmai Niljikar, Manish Gawali +3
We propose an automated method based on deep learning to compute the cardiothoracic ratio and detect the presence of cardiomegaly from chest radiographs. We develop two separate mo…
Comparison of Privacy-Preserving Distributed Deep Learning Methods in Healthcare
Manish Gawali, Arvind C S, Shriya Suryavanshi +5
In this paper, we compare three privacy-preserving distributed learning techniques: federated learning, split learning, and SplitFed. We use these techniques to develop binary clas…