Pith

open record

sign in

arxiv: 2407.11383 · v1 · pith:3HILILIP · submitted 2024-07-16 · cs.CV · cs.AI

TM-PATHVQA:90000+ Textless Multilingual Questions for Medical Visual Question Answering

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel pith:3HILILIPrecord.jsonopen to challenge →

classification cs.CV cs.AI
keywords datasetmedicalmultilingualquestionsvisualansweringimagesinteraction
0
0 comments X
read the original abstract

In healthcare and medical diagnostics, Visual Question Answering (VQA) mayemergeasapivotal tool in scenarios where analysis of intricate medical images becomes critical for accurate diagnoses. Current text-based VQA systems limit their utility in scenarios where hands-free interaction and accessibility are crucial while performing tasks. A speech-based VQA system may provide a better means of interaction where information can be accessed while performing tasks simultaneously. To this end, this work implements a speech-based VQA system by introducing a Textless Multilingual Pathological VQA (TMPathVQA) dataset, an expansion of the PathVQA dataset, containing spoken questions in English, German & French. This dataset comprises 98,397 multilingual spoken questions and answers based on 5,004 pathological images along with 70 hours of audio. Finally, this work benchmarks and compares TMPathVQA systems implemented using various combinations of acoustic and visual features.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Multilingual Hematology Visual Question Answering Dataset

    cs.CV 2026-06 unverdicted novelty 6.0

    Introduces WBCMor VQA benchmark with 110K bilingual QA pairs for hematology VQA on 20K cell images using existing annotations and a new Urdu dictionary.