TY  - JOUR
N2  - 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.
ID  - scholars13914
EP  - 182
UR  - 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
SN  - 18650929
JF  - Communications in Computer and Information Science
N1  - cited By 7; Conference of 4th International Conference on Computer Vision and Image Processing, CVIP 2019 ; Conference Date: 27 September 2019 Through 29 September 2019; Conference Code:238879
TI  - Automatic report generation for chest X-Ray images: A multilevel multi-attention approach
VL  - 1147 C
KW  - Behavioral research; Computer vision; Convolutional neural networks; Decoding; Signal encoding
KW  -  Chest X-ray image; Course information; Medical professionals; Radiology reports; Sequential dependencies; Short term memory; State-of-the-art methods; Time-consuming tasks
KW  -  Medical imaging
A1  - Gajbhiye, G.O.
A1  - Nandedkar, A.V.
A1  - Faye, I.
SP  - 174
PB  - Springer
AV  - none
Y1  - 2020///
ER  -