X-ray Classification

Benchmarks archive 2025-07-28

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Datasets archive 2025-07-28

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Most implemented papers archive 2025-07-28

27 shown of 27 papers with code (70 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Expanding the Horizon: Enabling Hybrid Quantum Transfer Learning for Long-Tailed Chest X-Ray Classification
  30 Apr 2024 2 repositories listed
  We evaluated the performance of our Jax-based framework in terms of efficiency and performance for hybrid quantum transfer learning for long-tailed classification across 8, 14, and 19 disease labels using large-scale…
• From Isolation to Collaboration: Federated Class-Heterogeneous Learning for Chest X-Ray Classification
  17 Jan 2023 2 repositories listed
  We propose surgical aggregation, a FL method that uses selective aggregation to collaboratively train a global model using distributed, class-heterogeneous datasets.
• Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-Ray Data
  6 May 2022 2 repositories listed
  We demonstrate that both model architectures are vulnerable to privacy violation by applying image reconstruction attacks to local model updates from individual clients.
• BoMD: Bag of Multi-label Descriptors for Noisy Chest X-ray Classification
  3 Mar 2022 2 repositories listed
  Deep learning methods have shown outstanding classification accuracy in medical imaging problems, which is largely attributed to the availability of large-scale datasets manually annotated with clean labels.
• DeepChest: Dynamic Gradient-Free Task Weighting for Effective Multi-Task Learning in Chest X-ray Classification
  29 May 2025 1 repository listed
  While Multi-Task Learning (MTL) offers inherent advantages in complex domains such as medical imaging by enabling shared representation learning, effectively balancing task contributions remains a significant challenge.
• MedCAM-OsteoCls: Medical Context Aware Multimodal Classification of Knee Osteoarthritis
  7 Mar 2025 1 repository listed
  Knee Osteoarthritis (KOA) is a degenerative musculoskeletal joint disorder that significantly impacts middle-aged and elderly individuals.
• Improving the Efficiency of Self-Supervised Adversarial Training through Latent Clustering-Based Selection
  15 Jan 2025 1 repository listed Syntology ran 5 of 5 samples · 0 unverified · 1 pointer-only (licence)
  Recent works propose the use of self-supervised adversarial training (SSAT) with external or synthetically generated unlabeled data to enhance model robustness.
• Tackling Data Heterogeneity in Federated Learning via Loss Decomposition
  22 Aug 2024 1 repository listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  To mitigate the impact of data heterogeneity on FL performance, we start with analyzing how FL training influence FL performance by decomposing the global loss into three terms: local loss, distribution shift loss and…
• BiasPruner: Debiased Continual Learning for Medical Image Classification
  11 Jul 2024 1 repository listed
  Utilizing a new bias score that measures the contribution of each unit in the network to learning spurious features, BiasPruner prunes those units with the highest bias scores to form a debiased subnetwork preserved for…
• Human Expertise in Algorithmic Prediction
  1 Feb 2024 1 repository listed
  Algorithmic indistinguishability yields a natural test for assessing whether experts incorporate this kind of "side information", and further provides a simple but principled method for selectively incorporating human…
• A Single Graph Convolution Is All You Need: Efficient Grayscale Image Classification
  1 Feb 2024 1 repository listed
  Experimental results on benchmark grayscale image datasets demonstrate the effectiveness of our approach, achieving significantly lower latency (up to 16× less on MSTAR) and competitive or superior performance compared…
• Few-shot learning for COVID-19 Chest X-Ray Classification with Imbalanced Data: An Inter vs. Intra Domain Study
  18 Jan 2024 1 repository listed
  For this, we propose a methodology based on Siamese neural networks in which a series of techniques are integrated to mitigate the effects of data scarcity and distribution imbalance.
• UniChest: Conquer-and-Divide Pre-training for Multi-Source Chest X-Ray Classification
  18 Dec 2023 1 repository listed Syntology ran 7 of 8 samples · 1 unverified
  Vision-Language Pre-training (VLP) that utilizes the multi-modal information to promote the training efficiency and effectiveness, has achieved great success in vision recognition of natural domains and shown promise in…
• SynthEnsemble: A Fusion of CNN, Vision Transformer, and Hybrid Models for Multi-Label Chest X-Ray Classification
  13 Nov 2023 1 repository listed
  Chest X-rays are widely used to diagnose thoracic diseases, but the lack of detailed information about these abnormalities makes it challenging to develop accurate automated diagnosis systems, which is crucial for early…
• SEDA: Self-Ensembling ViT with Defensive Distillation and Adversarial Training for robust Chest X-rays Classification
  15 Aug 2023 1 repository listed
  Deep Learning methods have recently seen increased adoption in medical imaging applications.
• CheXFusion: Effective Fusion of Multi-View Features using Transformers for Long-Tailed Chest X-Ray Classification
  8 Aug 2023 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  Our success in the task underscores the significance of considering multi-view settings, class imbalance, and label co-occurrence in medical image classification.
• GazeGNN: A Gaze-Guided Graph Neural Network for Chest X-ray Classification
  29 May 2023 1 repository listed
  To our best knowledge, GazeGNN is the first work that adopts GNN to integrate image and eye-gaze data.
• Multi-Label Chest X-Ray Classification via Deep Learning
  27 Nov 2022 1 repository listed
  The goal of this paper is to develop a lightweight solution to detect 14 different chest conditions from an X ray image.
• Auto-outlier Fusion Technique for Chest X-ray classification with Multi-head Attention Mechanism
  15 Nov 2022 1 repository listed
  A chest X-ray is one of the most widely available radiological examinations for diagnosing and detecting various lung illnesses.
• A knee cannot have lung disease: out-of-distribution detection with in-distribution voting using the medical example of chest X-ray classification
  1 Aug 2022 1 repository listed
  The proposed IDV approach trained on ID (chest X-ray 14) and OOD data (IRMA and ImageNet) achieved, on average, 0.
• Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging
  17 May 2022 1 repository listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  The collection and curation of large-scale medical datasets from multiple institutions is essential for training accurate deep learning models, but privacy concerns often hinder data sharing.
• Pseudo Bias-Balanced Learning for Debiased Chest X-ray Classification
  18 Mar 2022 1 repository listed
  Deep learning models were frequently reported to learn from shortcuts like dataset biases.
• Heuristic Hyperparameter Optimization for Convolutional Neural Networks using Genetic Algorithm
  14 Dec 2021 1 repository listed
  In the past few years, deep learning has proven to be one of the most powerful methods in the field of image classification.
• Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations
  29 Jun 2021 1 repository listed
  NURD finds a representation from this set that is most informative of the label under the nuisance-randomized distribution, and we prove that this representation achieves the highest performance regardless of the…
• Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification
  5 Mar 2021 1 repository listed
  In this paper, we propose Self-supervised Mean Teacher for Semi-supervised (S²MTS²) learning that combines self-supervised mean-teacher pre-training with semi-supervised fine-tuning.
• Big Self-Supervised Models Advance Medical Image Classification
  13 Jan 2021 1 repository listed
  Self-supervised pretraining followed by supervised fine-tuning has seen success in image recognition, especially when labeled examples are scarce, but has received limited attention in medical image analysis.
• Emergent symbolic language based deep medical image classification
  22 Aug 2020 1 repository listed
  In this work, we demonstrate for the first time, the emer-gence of deep symbolic representations of emergent language in the frame-work of image classification.

Syntology lines on 5 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.