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Novel loss functions for ensemble-based medical image classification — Europe PMC PMID:34968393

Source: Europe PMC (en)
Original: https://europepmc.org/article/MED/34968393
DOI: 10.1371/journal.pone.0261307
License (as archived): CC0-1.0
Archived: 2026-10-31T18:59:25.287Z
SHA-256 of the archived text: 3f0249dd55cbc3fe6b1334295d5af8175af2100a974281cb36bf01728c3d4e93
Stable URL of this page: https://metraipolis.com/documento/dc_c190818ce189

How to cite

Novel loss functions for ensemble-based medical image classification — Europe PMC PMID:34968393 — Europe PMC (en), https://europepmc.org/article/MED/34968393 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_c190818ce189 (Metraipolis archive dc_c190818ce189, sha256 3f0249dd55cbc3fe6b1334295d5af8175af2100a974281cb36bf01728c3d4e93)

BibTeX

@misc{dcc190818ce189,
  title = {Novel loss functions for ensemble-based medical image classification — Europe PMC PMID:34968393},
  howpublished = {Europe PMC (en)},
  url = {https://europepmc.org/article/MED/34968393},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_c190818ce189; sha256 3f0249dd55cbc3fe6b1334295d5af8175af2100a974281cb36bf01728c3d4e93},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: Europe PMC (en), https://europepmc.org/article/MED/34968393. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Medical images commonly exhibit multiple abnormalities. Predicting them requires multi-class classifiers whose training and desired reliable performance can be affected by a combination of factors, such as, dataset size, data source, distribution, and the loss function used to train deep neural networks. Currently, the cross-entropy loss remains the de-facto loss function for training deep learning classifiers. This loss function, however, asserts equal learning from all classes, leading to a bias toward the majority class. Although the choice of the loss function impacts model performance, to the best of our knowledge, we observed that no literature exists that performs a comprehensive analysis and selection of an appropriate loss function toward the classification task under study. In this work, we benchmark various state-of-the-art loss functions, critically analyze model performance, and propose improved loss functions for a multi-class classification task. We select a pediatric chest X-ray (CXR) dataset that includes images with no abnormality (normal), and those exhibiting manifestations consistent with bacterial and viral pneumonia. We construct prediction-level and model-level ensembles to improve classification performance. Our results show that compared to the individual models and the state-of-the-art literature, the weighted averaging of the predictions for top-3 and top-5 model-level ensembles delivered significantly superior classification performance (p < 0.05) in terms of MCC (0.9068, 95% confidence interval (0.8839, 0.9297)) metric. Finally, we performed localization studies to interpret model behavior and confirm that the individual models and ensembles learned task-specific features and highlighted disease-specific regions of interest. The code is available at https://github.com/sivaramakrishnan-rajaraman/multiloss_ensemble_models.

Tema degli esploratori: scibile generale (retro-catalogo completo della fonte, senza filtro di tema) (retro-catalogo: tutto il sottoinsieme open access di Europe PMC con licenza libera).
Pubblicazione: PloS one, 2021 (prima pubblicazione 2021-12-30).
Identificativi: PMID 34968393 · PMCID PMC8718001 · DOI 10.1371/journal.pone.0261307.
Descrittori MeSH principali: Diagnostic Imaging; Algorithms.
Pagina: https://europepmc.org/article/MED/34968393 — https://doi.org/10.1371/journal.pone.0261307. Autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza dell'articolo dichiarata da Europe PMC: CC0 (la versione è indicata dall'editore sulla pagina). Abstract riportato senza modifiche al contenuto, con attribuzione; deposito marcato CC0.
Letto il 2026-10-31T18:51:05.856Z tramite l'API REST ufficiale di Europe PMC (EMBL-EBI), nel rispetto del robots.txt e del crawl-delay.