activity
20162021
most citedLeveraging Medical Visual Question Answering with Supporting Facts

9 citations · 10 across the 3 of their papers we have counts for

collaborators

7 papers

cs.CL2021

Towards Clinical Encounter Summarization: Learning to Compose Discharge Summaries from Prior Notes

Han-Chin Shing, Chaitanya Shivade, Nima Pourdamghani +4

The records of a clinical encounter can be extensive and complex, thus placing a premium on tools that can extract and summarize relevant information. This paper introduces the tas…

cs.CL2021

Neural Inverse Text Normalization

Monica Sunkara, Chaitanya Shivade, Sravan Bodapati +1

While there have been several contributions exploring state of the art techniques for text normalization, the problem of inverse text normalization (ITN) remains relatively unexplo…

cs.AI20201 cited

Receptivity of an AI Cognitive Assistant by the Radiology Community: A Report on Data Collected at RSNA

Karina Kanjaria, Anup Pillai, Chaitanya Shivade +4

Due to advances in machine learning and artificial intelligence (AI), a new role is emerging for machines as intelligent assistants to radiologists in their clinical workflows. But…

cs.CV20199 cited

Leveraging Medical Visual Question Answering with Supporting Facts

Tomasz Kornuta, Deepta Rajan, Chaitanya Shivade +2

In this working notes paper, we describe IBM Research AI (Almaden) team's participation in the ImageCLEF 2019 VQA-Med competition. The challenge consists of four question-answering…

cs.CL2019

Towards Automatic Generation of Shareable Synthetic Clinical Notes Using Neural Language Models

Oren Melamud, Chaitanya Shivade

Large-scale clinical data is invaluable to driving many computational scientific advances today. However, understandable concerns regarding patient privacy hinder the open dissemin…

cs.CL2018

Lessons from Natural Language Inference in the Clinical Domain

Alexey Romanov, Chaitanya Shivade

State of the art models using deep neural networks have become very good in learning an accurate mapping from inputs to outputs. However, they still lack generalization capabilitie…