# Multi-loss ensemble deep learning for chest X-ray classification

arXiv: https://arxiv.org/abs/2109.14433 · HTML: https://mlsanity.com/paper/2109.14433/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 58 references that are in the map.
- First posted on arXiv 29 Sep 2021.

## Builds on (most cited first, 19 of 19)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Understanding deep learning requires rethinking generalization](https://mlsanity.com/paper/1611.03530/) (2016)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1708.08987/) (2017)
- [Tversky loss function for image segmentation using 3D fully convolutional deep networks](https://mlsanity.com/paper/1706.05721/) (2017)
- [A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation](https://mlsanity.com/paper/1810.07842/) (2018)
- [Delving Deep into Label Smoothing](https://mlsanity.com/paper/2011.12562/) (2020)
- [A scoping review of transfer learning research on medical image analysis using ImageNet](https://mlsanity.com/paper/2004.13175/) (2020)
- [Improved Trainable Calibration Method for Neural Networks on Medical Imaging Classification](https://mlsanity.com/paper/2009.04057/) (2020)
- [Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rays](https://mlsanity.com/paper/2004.08379/) (2020)
- [Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1705.09850/) (2017)
- [ECOVNet: An Ensemble of Deep Convolutional Neural Networks Based on EfficientNet to Detect COVID-19 From Chest X-rays](https://mlsanity.com/paper/2009.11850/) (2020)
- [Unified Representation Learning for Efficient Medical Image Analysis](https://mlsanity.com/paper/2006.11223/) (2020)
- [Ensembles of Convolutional Neural Networks models for pediatric pneumonia diagnosis](https://mlsanity.com/paper/2010.02007/) (2020)
- [FLANNEL: Focal Loss Based Neural Network Ensemble for COVID-19 Detection](https://mlsanity.com/paper/2010.16039/) (2020)

## Built on by (most cited first, 1 of 1)

- [Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive Learning](https://mlsanity.com/paper/2312.14222/) (2023)

## Abstract (arXiv)

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 the 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. In this work, we benchmark various state-of-the-art loss functions that are suitable for multi-class classification, critically analyze model performance, and propose improved loss functions. 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, respectively, 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 behaviors to visualize and confirm that the individual models and ensembles learned meaningful features and highlighted disease manifestations.