@article{discovery10193306,
           title = {Diagnosing Necrotising Enterocolitis Via Fine-Grained Visual Classification},
         journal = {IEEE Transactions on Biomedical Engineering},
            year = {2024},
       publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
           pages = {3160  --3169},
            note = {This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.},
          volume = {71},
          number = {11},
           month = {November},
        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 {\ensuremath{<}} 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.},
             url = {http://dx.doi.org/10.1109/tbme.2024.3409642},
            issn = {0018-9294},
          author = {Yung, Ka-Wai and Sivaraj, Jayaram and De Coppi, Paolo and Stoyanov, Danail and Loukogeorgakis, Stavros and Mazomenos, Evangelos B},
        keywords = {Necrotizing Enterocolitis, Fine Grained Visual Classification, Abdominal X-ray}
}