Search Results for author: Asma Ben Abacha

Found 18 papers, 8 papers with code

Overview of the MEDIQA 2021 Shared Task on Summarization in the Medical Domain

no code implementations NAACL (BioNLP) 2021 Asma Ben Abacha, Yassine Mrabet, Yuhao Zhang, Chaitanya Shivade, Curtis Langlotz, Dina Demner-Fushman

The MEDIQA 2021 shared tasks at the BioNLP 2021 workshop addressed three tasks on summarization for medical text: (i) a question summarization task aimed at exploring new approaches to understanding complex real-world consumer health queries, (ii) a multi-answer summarization task that targeted aggregation of multiple relevant answers to a biomedical question into one concise and relevant answer, and (iii) a radiology report summarization task addressing the development of clinically relevant impressions from radiology report findings.

Text Summarization

Evidence-based Fact-Checking of Health-related Claims

1 code implementation Findings (EMNLP) 2021 Mourad Sarrouti, Asma Ben Abacha, Yassine Mrabet, Dina Demner-Fushman

Our experiments showed that training deep learning models on real-world medical claims greatly improves performance compared to models trained on synthetic and open-domain claims.

Fact Checking

3D-MIR: A Benchmark and Empirical Study on 3D Medical Image Retrieval in Radiology

no code implementations23 Nov 2023 Asma Ben Abacha, Alberto Santamaria-Pang, Ho Hin Lee, Jameson Merkow, Qin Cai, Surya Teja Devarakonda, Abdullah Islam, Julia Gong, Matthew P. Lungren, Thomas Lin, Noel C Codella, Ivan Tarapov

The increasing use of medical imaging in healthcare settings presents a significant challenge due to the increasing workload for radiologists, yet it also offers opportunity for enhancing healthcare outcomes if effectively leveraged.

Medical Image Retrieval Retrieval

ACI-BENCH: a Novel Ambient Clinical Intelligence Dataset for Benchmarking Automatic Visit Note Generation

no code implementations3 Jun 2023 Wen-wai Yim, Yujuan Fu, Asma Ben Abacha, Neal Snider, Thomas Lin, Meliha Yetisgen

Here we present the Ambient Clinical Intelligence Benchmark (ACI-BENCH) corpus, the largest dataset to date tackling the problem of AI-assisted note generation from visit dialogue.


An Investigation of Evaluation Metrics for Automated Medical Note Generation

1 code implementation27 May 2023 Asma Ben Abacha, Wen-wai Yim, George Michalopoulos, Thomas Lin

To study the correlation between the automatic metrics and manual judgments, we evaluate automatic notes/summaries by comparing the system and reference facts and computing the factual correctness, and the hallucination and omission rates for critical medical facts.

Knowledge Graph Embedding Text Summarization

Visual Question Generation from Radiology Images

1 code implementation WS 2020 Mourad Sarrouti, Asma Ben Abacha, Dina Demner-Fushman

Visual Question Generation (VQG), the task of generating a question based on image contents, is an increasingly important area that combines natural language processing and computer vision.

Image Augmentation Question Generation +3

Question-Driven Summarization of Answers to Consumer Health Questions

1 code implementation18 May 2020 Max Savery, Asma Ben Abacha, Soumya Gayen, Dina Demner-Fushman

This dataset can be used to evaluate single or multi-document summaries generated by algorithms using extractive or abstractive approaches.

Question Answering

Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question Answering

1 code implementation WS 2019 Asma Ben Abacha, Chaitanya Shivade, Dina Demner-Fushman

MEDIQA 2019 includes three tasks: Natural Language Inference (NLI), Recognizing Question Entailment (RQE), and Question Answering (QA) in the medical domain.

Information Retrieval Natural Language Inference +2

A Question-Entailment Approach to Question Answering

3 code implementations23 Jan 2019 Asma Ben Abacha, Dina Demner-Fushman

One of the challenges in large-scale information retrieval (IR) is to develop fine-grained and domain-specific methods to answer natural language questions.

Information Retrieval Question Answering +2

A dataset of clinically generated visual questions and answers about radiology images

no code implementations Scientific Data 2018 Jason J. Lau, Soumya Gayen, Asma Ben Abacha, Dina Demner-Fushman

We introduce VQA-RAD, the first manually constructed dataset where clinicians asked naturally occurring questions about radiology images and provided reference answers.

Decision Making Medical Visual Question Answering +2

Cannot find the paper you are looking for? You can Submit a new open access paper.