eprintid: 10193306
rev_number: 8
eprint_status: archive
userid: 699
dir: disk0/10/19/33/06
datestamp: 2024-06-12 10:14:19
lastmod: 2024-12-11 09:29:28
status_changed: 2024-06-12 10:14:19
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Yung, Ka-Wai
creators_name: Sivaraj, Jayaram
creators_name: De Coppi, Paolo
creators_name: Stoyanov, Danail
creators_name: Loukogeorgakis, Stavros
creators_name: Mazomenos, Evangelos B
title: Diagnosing Necrotising Enterocolitis Via Fine-Grained Visual Classification
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
divisions: F42
keywords: Necrotizing Enterocolitis, Fine Grained Visual Classification, Abdominal X-ray
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
abstract: Necrotizing Enterocolitis (NEC) is a devastating condition affecting prematurely born neonates. Reviewing Abdominal X-rays (AXRs) is a key step in NEC diagnosis, staging and treatment decision-making, but poses significant challenges due to the subtle, difficult-to-identify radiological signs of the disease. In this paper, we propose AIDNEC - AI D iagnosis of NEC rotizing enterocolitis, a deep learning method to automatically detect and stratify the severity (surgical or medical) of NEC from no pathology in AXRs. The model is trainable end-to-end and integrates a Detection Transformer and Graph Convolution modules for localizing discriminative areas in AXRs, used to formulate subtle local embeddings. These are then combined with global image features to perform Fine-Grained Visual Classification (FGVC). We evaluate AIDNEC on our GOSH NEC dataset of 1153 images from 334 patients, achieving 79.7% accuracy in classifying NEC against No Pathology. AIDNEC outperforms the backbone by 2.6%, FGVC models by 2.5% and CheXNet by 4.2%, with statistically significant (two-tailed p < 0.05) improvements, while providing meaningful discriminative regions to support the classification decision. Additional validation in the publicly available Chest X-ray14 dataset yields comparable performance to state-of-the-art methods, illustrating AIDNEC's robustness in a different X-ray classification task. Dataset and source code will be released in our institutional database.
date: 2024-11
date_type: published
publisher: Institute of Electrical and Electronics Engineers (IEEE)
official_url: http://dx.doi.org/10.1109/tbme.2024.3409642
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2283978
doi: 10.1109/tbme.2024.3409642
lyricists_name: Mazomenos, Evangelos
lyricists_name: Stoyanov, Danail
lyricists_id: EMAZO45
lyricists_id: DSTOY26
actors_name: Flynn, Bernadette
actors_id: BFFLY94
actors_role: owner
full_text_status: public
publication: IEEE Transactions on Biomedical Engineering
volume: 71
number: 11
pagerange: 3160  -3169
issn: 0018-9294
citation:        Yung, Ka-Wai;    Sivaraj, Jayaram;    De Coppi, Paolo;    Stoyanov, Danail;    Loukogeorgakis, Stavros;    Mazomenos, Evangelos B;      (2024)    Diagnosing Necrotising Enterocolitis Via Fine-Grained Visual Classification.                   IEEE Transactions on Biomedical Engineering , 71  (11)   3160 -3169.    10.1109/tbme.2024.3409642 <https://doi.org/10.1109/tbme.2024.3409642>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10193306/1/Diagnosing_Necrotising_Enterocolitis_Via_Fine-Grained_Visual_Classification.pdf