relation: https://khub.utp.edu.my/scholars/13914/
title: Automatic report generation for chest X-Ray images: A multilevel multi-attention approach
creator: Gajbhiye, G.O.
creator: Nandedkar, A.V.
creator: Faye, I.
description: A comprehensive X-ray imaging report greatly assists the medical professional to investigate an indispensable condition and medication. The preparation of an extensive and diversified medical report by analysing the chest X-ray image is a time-consuming task and requires highly experienced professionals. This work targets the fundamental problem of generating a long and multifarious medical report for the chest X-ray image. It introduces a novel Multilevel Multi-Attention based encoder-decoder approach by combining Context Level Visual Attention and Textual Attention to generate a plausible medical report for different views of chest X-ray images. It exploited the proven ability of the Convolutional Neural Network to acquire course information of visual-spatial regions as an encoder. It leverages the strength of the Long Short-Term Memory network to learn long sequential dependencies and the ability of attention to focus on the prominent section as a decoder. The proposed method emphasizes on contextual coherence in intra and inter-sentence dependency within a report to improve the overall medical report generation quality. The effectiveness of the proposed model is evaluated on the publicly available IU chest X-ray dataset consisting of chest images along with multifarious radiology reports. The final performance of the proposed model is reported using the COCO-caption evaluation API. It shows a significant improvement in a medical report generation task compared to state-of-the-art methods. © Springer Nature Singapore Pte Ltd 2020.
publisher: Springer
date: 2020
type: Article
type: PeerReviewed
identifier:   Gajbhiye, G.O. and Nandedkar, A.V. and Faye, I.  (2020) Automatic report generation for chest X-Ray images: A multilevel multi-attention approach.  Communications in Computer and Information Science, 1147 C.  pp. 174-182.  ISSN 18650929     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083724536&doi=10.1007%2f978-981-15-4015-8_15&partnerID=40&md5=e28028f91eaaf6e681e9fa1574c112b0
relation: 10.1007/978-981-15-4015-8₁₅
identifier: 10.1007/978-981-15-4015-8₁₅