Handling Supervision Scarcity in Chest X-ray Classification: Long-Tailed and Zero-Shot Learning

• URL: http://arxiv.org/abs/2602.13430v1
• Date: Fri, 13 Feb 2026 20:07:34 GMT
• Title: Handling Supervision Scarcity in Chest X-ray Classification: Long-Tailed and Zero-Shot Learning
• Authors: Ha-Hieu Pham, Hai-Dang Nguyen, Thanh-Huy Nguyen, Min Xu, Ulas Bagci, Trung-Nghia Le, Huy-Hieu Pham,
• Abstract summary: The CXR-LT 2026 challenge addresses issues on a PadChest-based benchmark with a 36-class label space split into 30 in-distribution classes for training and 6 out-of-distribution classes for zero-shot evaluation.<n>We present task-specific solutions tailored to the distinct supervision regimes.<n>For Task 1 (long-tailed multi-label classification), we adopt an imbalance-aware multi-label learning strategy to improve recognition of tail classes while maintaining stable performance on frequent findings.<n>For Task 2 (zero-shot OOD recognition), we propose a prediction approach that produces scores for unseen disease categories without using any supervised labels
• Score: 14.888577410967129
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Chest X-ray (CXR) classification in clinical practice is often limited by imperfect supervision, arising from (i) extreme long-tailed multi-label disease distributions and (ii) missing annotations for rare or previously unseen findings. The CXR-LT 2026 challenge addresses these issues on a PadChest-based benchmark with a 36-class label space split into 30 in-distribution classes for training and 6 out-of-distribution (OOD) classes for zero-shot evaluation. We present task-specific solutions tailored to the distinct supervision regimes. For Task 1 (long-tailed multi-label classification), we adopt an imbalance-aware multi-label learning strategy to improve recognition of tail classes while maintaining stable performance on frequent findings. For Task 2 (zero-shot OOD recognition), we propose a prediction approach that produces scores for unseen disease categories without using any supervised labels or examples from the OOD classes during training. Evaluated with macro-averaged mean Average Precision (mAP), our method achieves strong performance on both tasks, ranking first on the public leaderboard of the development phase. Code and pre-trained models are available at https://github.com/hieuphamha19/CXR_LT.

Related papers

• Loss Design and Architecture Selection for Long-Tailed Multi-Label Chest X-Ray Classification [0.0]
  Longtailed distributions class pose a significant challenge for multi-label chest X-ray classification.<n>We present a systematic empirical evaluation of loss functions, CNN backbone architectures and post-training strategies on the CXR-LT 2026 benchmark.<n>Our experiments demonstrate that LDAM with deferred re-weighting consistently outperforms standard BCE and asymmetric losses for rare class recognition.
  arXiv  Detail & Related papers  (2026-03-02T17:33:00Z)
• Overview of the CXR-LT 2026 Challenge: Multi-Center Long-Tailed and Zero Shot Chest X-ray Classification [14.263392973355666]
  We present the CXR-LT 2026 challenge.<n>This third iteration of the benchmark introduces a multi-center dataset comprising over 145,000 images from PadChest and NIH Chest X-ray datasets.<n>The challenge defines two core tasks: (1) Robust Multi-Label Classification on 30 known classes and (2) Open-World Generalization to 6 unseen (out-of-distribution) rare disease classes.<n>We report the results of the top-performing teams, evaluating them via mean Average Precision (mAP), AUROC, and F1-score.
  arXiv  Detail & Related papers  (2026-02-25T16:39:21Z)
• Cough Classification using Few-Shot Learning [0.7136933021609079]
  We leverage Prototypical Networks with spectrogram representations of cough sounds to address the challenge of limited labeled data.<n>Our study evaluates whether few-shot learning can enable models to achieve performance comparable to traditional deep learning approaches.<n> Experimental findings show that few-shot learning models can achieve competitive accuracy.
  arXiv  Detail & Related papers  (2025-09-11T14:56:47Z)
• CXR-CML: Improved zero-shot classification of long-tailed multi-label diseases in Chest X-Rays [3.196204482566275]
  Class imbalance in the distribution of clinical findings presents a significant challenge for self-supervised deep learning models.<n>We propose a class-weighting mechanism that directly aligns with the distribution of classes within the latent space.<n>Our approach results in a notable average improvement of 7% points in zero-shot AUC scores across 40 classes in the MIMIC-CXR-JPG dataset.
  arXiv  Detail & Related papers  (2025-07-25T16:05:47Z)
• Looking Beyond What You See: An Empirical Analysis on Subgroup Intersectional Fairness for Multi-label Chest X-ray Classification Using Social Determinants of Racial Health Inequities [4.351859373879489]
  Inherited biases in deep learning models can lead to disparities in prediction accuracy across protected groups. We propose a framework to achieve accurate diagnostic outcomes and ensure fairness across intersectional groups.
  arXiv  Detail & Related papers  (2024-03-27T02:13:20Z)
• SLCA: Slow Learner with Classifier Alignment for Continual Learning on a Pre-trained Model [73.80068155830708]
  We present an extensive analysis for continual learning on a pre-trained model (CLPM) We propose a simple but extremely effective approach named Slow Learner with Alignment (SLCA) Across a variety of scenarios, our proposal provides substantial improvements for CLPM.
  arXiv  Detail & Related papers  (2023-03-09T08:57:01Z)
• Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study [75.05049024176584]
  We present a benchmark study of the long-tailed learning problem in the specific domain of thorax diseases on chest X-rays. We focus on learning from naturally distributed chest X-ray data, optimizing classification accuracy over not only the common "head" classes, but also the rare yet critical "tail" classes. The benchmark consists of two chest X-ray datasets for 19- and 20-way thorax disease classification, containing classes with as many as 53,000 and as few as 7 labeled training images.
  arXiv  Detail & Related papers  (2022-08-29T04:34:15Z)
• Learning Discriminative Representation via Metric Learning for Imbalanced Medical Image Classification [52.94051907952536]
  We propose embedding metric learning into the first stage of the two-stage framework specially to help the feature extractor learn to extract more discriminative feature representations. Experiments mainly on three medical image datasets show that the proposed approach consistently outperforms existing onestage and two-stage approaches.
  arXiv  Detail & Related papers  (2022-07-14T14:57:01Z)
• Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition [80.07843757970923]
  We show that existing OOD detection methods suffer from significant performance degradation when the training set is long-tail distributed. We propose Partial and Asymmetric Supervised Contrastive Learning (PASCL), which explicitly encourages the model to distinguish between tail-class in-distribution samples and OOD samples. Our method outperforms previous state-of-the-art method by $1.29%$, $1.45%$, $0.69%$ anomaly detection false positive rate (FPR) and $3.24%$, $4.06%$, $7.89%$ in-distribution
  arXiv  Detail & Related papers  (2022-07-04T01:53:07Z)
• Prototypical Classifier for Robust Class-Imbalanced Learning [64.96088324684683]
  We propose textitPrototypical, which does not require fitting additional parameters given the embedding network. Prototypical produces balanced and comparable predictions for all classes even though the training set is class-imbalanced. We test our method on CIFAR-10LT, CIFAR-100LT and Webvision datasets, observing that Prototypical obtains substaintial improvements compared with state of the arts.
  arXiv  Detail & Related papers  (2021-10-22T01:55:01Z)
• Exploring Classification Equilibrium in Long-Tailed Object Detection [29.069986049436157]
  We propose to use the mean classification score to indicate the classification accuracy for each category during training. We balance the classification via an Equilibrium Loss (EBL) and a Memory-augmented Feature Sampling (MFS) method. It improves the detection performance of tail classes by 15.6 AP, and outperforms the most recent long-tailed object detectors by more than 1 AP.
  arXiv  Detail & Related papers  (2021-08-17T08:39:04Z)
• Few-shot Action Recognition with Prototype-centered Attentive Learning [88.10852114988829]
  Prototype-centered Attentive Learning (PAL) model composed of two novel components. First, a prototype-centered contrastive learning loss is introduced to complement the conventional query-centered learning objective. Second, PAL integrates a attentive hybrid learning mechanism that can minimize the negative impacts of outliers.
  arXiv  Detail & Related papers  (2021-01-20T11:48:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.