The CXR‑LT 2026 Challenge introduces a multi‑center, long‑tailed chest X‑ray classification benchmark with over 145,000 radiologist‑annotated images from PadChest and NIH datasets. It defines two core tasks: robust multi‑label classification on 30 known classes and open‑world generalization to 6 unseen rare disease classes. The paper outlines data collection, annotation, solution strategies, and evaluates performance across head‑vs‑tail, calibration, and cross‑center gaps, noting that vision‑language models improve in‑distribution and zero‑shot performance but rare‑finding detection under multi‑center shift remains difficult.
arXiv:2608.24281v1 Announce Type: new Abstract: Reducing annotation requirements remains a key challenge in developing robust medical object detectors. To address this, Vision-Language (VL) object de...
arXiv:2607. 00975v1 Announce Type: cross Abstract: Chest X-ray multi-label classification is a core task in intelligent medical imaging diagnosis.
arXiv:2607. 05628v1 Announce Type: cross Abstract: Accurate and efficient classification of thoracic diseases in chest X-ray (CXR) images is crucial for timely diagnosis and treatment.
Med-AR introduces two autoregressive vision‑language models, Med‑AR‑8B and Med‑AR‑2B, pretrained on structured radiology reports, abnormality‑focused text, and region annotations to address long‑tailed chest X‑ray classification. The models outperform existing contrastive, self‑supervised, and supervised encoders—including Med‑CLIP, CheXFound, EVA‑Base, ARK, and BioViL‑T—across PadChest, MIMIC‑CXR, and CheXpert, achieving higher mean AUROC and AUPRC for head, medium, and tail findings and lower excess area under the risk‑coverage curve. Med‑AR also demonstrates improved selective‑prediction performance, with Med‑AR‑8B raising tail‑label mean AUPRC on MIMIC‑CXR from 0.1033 to 0.1441 and Med‑AR‑2B delivering the strongest discrimination on PadChest.
arXiv:2607. 26333v1 Announce Type: cross Abstract: Chest X-ray (CXR) machine learning relies heavily on automated evaluation using reference standards that aim to approximate clinical judgment.
The paper introduces a label‑free method called AURCC for selecting the best foundational model for medical image classification when the target domain lacks labels. AURCC uses a pseudo‑label discrepancy computed by the SUDO framework to score models without fine‑tuning. Experiments on chest X‑ray data across three inter‑hospital shifts show that AURCC closely matches the true model ranking, outperforming simple source‑accuracy baselines especially when source data are limited.
arXiv:2509. 19671v3 Announce Type: replace Abstract: Public datasets of Chest X-Rays (CXRs) have long been a popular benchmark for developing machine learning (ML) computer vision models in healthcare.
Large chest radiography archives are difficult to search because most studies are paired only with free-text reports rather than structured clinical annotations. Vision-language models offer a natural interface for text-to-image retrieval, but current biomedical models are primarily optimized for report-to-image matching rather than for satisfying short clinical search queries.
arXiv:2603.02790v2 Announce Type: replace Abstract: Foundation models are changing the way we develop medical artificial intelligence. By learning broadly generalizable features across diverse data m...
arXiv:2606. 16991v1 Announce Type: cross Abstract: Multiphasic contrast-enhanced CT (CECT) is widely used for abdominal lesion characterization, yet it carries inherent risks of contrast-induced nephropathy, escalates acquisition burden, and heavily contributes to radiologist workload.
The paper introduces Prototype Purification and Regulation (PPR), a multi‑label few‑shot learning framework for medical image classification that addresses two key limitations of existing metric‑based meta‑learning methods. PPR first purifies prototypes by using sample‑level comorbidity scores to highlight disease‑specific features, then regulates inter‑class prototype distances with disease‑level comorbidity statistics to create a comorbidity‑aware embedding space. Experiments on four chest X‑ray datasets, including cross‑domain tests, show that PPR outperforms state‑of‑the‑art methods, improving disease detection and demonstrating robust generalization and clinical applicability.
arXiv:2607. 20641v1 Announce Type: new Abstract: Federated learning (FL) enables multiple clinical institutions to collaboratively train a shared disease classifier without centralizing patient data.