The shared task addressed two of the challenges faced by medical video question answering: (I) a video classification task that explores new approaches to medical video understanding (labeling), and (ii) a visual answer localization task.
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.
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.
Deep neural networks have demonstrated high performance on many natural language processing (NLP) tasks that can be answered directly from text, and have struggled to solve NLP tasks requiring external (e. g., world) knowledge.
Toward this, this paper is focused on answering health-related questions asked by the public by providing visual answers from medical videos.
Pre-trained language models (PLMs) have proven to be effective for document re-ranking task.
Therefore, adapting this expert-level language into plain language versions is necessary for the public to reliably comprehend the vast health-related literature.
We extensively tested the proposed NNS approach and compared the performance with state-of-the-art NNS approaches on benchmark datasets and our created medical image datasets.
Clinical language processing has received a lot of attention in recent years, resulting in new models or methods for disease phenotyping, mortality prediction, and other tasks.
The quest for seeking health information has swamped the web with consumers' health-related questions.
This paper introduces a new challenge and datasets to foster research toward designing systems that can understand medical videos and provide visual answers to natural language questions.
The growth of online consumer health questions has led to the necessity for reliable and accurate question answering systems.
In this paper, we study the task of abstractive summarization for real-world consumer health questions.
We present an overview of the TREC-COVID Challenge, an information retrieval (IR) shared task to evaluate search on scientific literature related to COVID-19.
Efficient document summarization requires evaluation measures that can not only rank a set of systems based on an average score, but also highlight which individual summary is better than another.
Recent work has shown that pre-trained Transformers obtain remarkable performance on many natural language processing tasks including automatic summarization.
Automatic summarization research has traditionally focused on providing high quality general-purpose summaries of documents.
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.
This dataset can be used to evaluate single or multi-document summaries generated by algorithms using extractive or abstractive approaches.
TREC-COVID is a community evaluation designed to build a test collection that captures the information needs of biomedical researchers using the scientific literature during a pandemic.
In this paper we present OSCAR (Ontology-based Semantic Composition Augmented Regularization), a method for injecting task-agnostic knowledge from an Ontology or knowledge graph into a neural network during pretraining.
We define a representation framework for extracting spatial information from radiology reports (Rad-SpRL).
MEDIQA 2019 includes three tasks: Natural Language Inference (NLI), Recognizing Question Entailment (RQE), and Question Answering (QA) in the medical domain.
One of the challenges in large-scale information retrieval (IR) is to develop fine-grained and domain-specific methods to answer natural language questions.
We introduce VQA-RAD, the first manually constructed dataset where clinicians asked naturally occurring questions about radiology images and provided reference answers.
Tested on cQA-B-2016 test data, our RQE system outperformed the best system of the 2016 challenge in all measures with 77. 47 MAP and 80. 57 Accuracy.
Text similarity measures are used in multiple tasks such as plagiarism detection, information ranking and recognition of paraphrases and textual entailment.
Readers usually rely on abstracts to identify relevant medical information from scientific articles.
Recurrent neural networks (RNNs) are then trained to describe the contexts of a detected disease, based on the deep CNN features.
This paper presents a method for annotating question decomposition on complex medical questions.