arXiv Machine Learning

Taxlifier: Leveraging Disease Taxonomy for Enhanced Multi-Label Classification in Chest Radiography

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.

arXiv AI

Jul 2

TRCGL-Net: A Long-Tailed Multi-Label Chest X-Ray Classification Framework with Generative Data Augmentation and Label Co-Occurrence Modeling

arXiv:2607. 00975v1 Announce Type: cross Abstract: Chest X-ray multi-label classification is a core task in intelligent medical imaging diagnosis.

By Tong Shao, Hongshun Ling, Li Zhang, Jinjing Wu, Junke Wang, Yuan Gao, Fang Wang

diffusion benchmarks

arXiv Machine Learning

Jul 30

Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance

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.

By Panagiotis Fytas, Ian Selby, Clemens Karner, Judith Babar, Simon Baker, Jake Beckford, Timothy J. Sadler, Shahab Shahipasand, Arthikkaa Thavakumar, John Li Chen, Alex Sawer, Michael Roberts, Jonathan Weir-McCall, J. H. F. Rudd, Carola-Bibiane Sch\"onlieb, Anna Korhonen, Anna Breger

llms fine-tuning multimodal safety

arXiv AI

Jul 7

PulmoSight-XAI: An Explainable Multi-View Attention Ensemble with Gradient Boosting Meta-Learning for Multi-Label Chest X-Ray Classification

arXiv:2607. 04478v1 Announce Type: cross Abstract: Automated chest X-ray classification remains challenging due to severe class imbalance, co-occurring pathologies, and the loss of localized features in conventional architectures.

By Moshiur Rahman, Shafqat Alam, Tasnia Binte Mamun

benchmarks

arXiv Machine Learning

Jul 24

One Round Is All You Need: Analytic Federated Learning for Task-Heterogeneous Multi-Label Medical Image Classification

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.

By Afsaneh Mahanipour, Hana Khamfroush

computer-vision benchmarks safety

arXiv Machine Learning

Jun 29

Revisiting Performance Claims for Chest X-Ray Models Using Clinical Context

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.

By Andrew Wang, Jiashuo Zhang, Michael Oberst

computer-vision benchmarks

arXiv Machine Learning

Jul 13

From Classification to Localization and Clinical Validation: Large-Scale Development of a Deep Learning System for Thoracic Disease Detection on Chest Radiographs in Thailand

arXiv:2607. 09305v1 Announce Type: cross Abstract: Chest radiography (CXR) remains the most widely used thoracic imaging modality, yet expert interpretation is constrained by a severe shortage of radiologists in Thailand and across Southeast Asia.

By Isarun Chamveha, Tretap Promwiset, Napat Wanchaitanawong, Trongtum Tongdee, Pairash Saiviroonporn, Warasinee Chaisangmongkon

Hugging Face Trending Papers

Aug 11

RadFusion: Towards Threshold-Controllable Radiology Report Generation

Automated radiology report generation is advancing rapidly in response to the shortage of radiologists, yet unlike a perception model, existing generation models offer no control over the sensitivity-specificity trade-off of their diagnostic content. Such control is essential because clinical scenarios diverge: emergency triage prioritizes sensitivity to reduce missed findings, whereas confirmatory interpretation emphasizes specificity to limit unnecessary interventions.

llms

arXiv Machine Learning

Jun 4

Radiomic Feature Selection Using Gradient Loss of Deep Neural Network for Lung Cancer Stage Detection

arXiv:2606. 04453v1 Announce Type: cross Abstract: Radiomics enables extraction of quantitative imaging biomarkers from medical images and has become an important tool for computer-aided cancer diagnosis.

By Hina Shakir, Mohammad Mohatram, Javeed Hussain, Syed Rizwan Ali, Muhammad Irfan Memon

multimodal

arXiv AI

Aug 12

RadFusion: Towards Threshold-Controllable Radiology Report Generation

arXiv:2608. 10505v1 Announce Type: new Abstract: Automated radiology report generation is advancing rapidly in response to the shortage of radiologists, yet unlike a perception model, existing generation models offer no control over the sensitivity-specificity trade-off of their diagnostic content.

By Ying Jin, Noel C. F. Codella, John Corring, Mu Wei, Dinei Florencio, Eric Horvitz

llms

arXiv AI

Jul 7

Comparison of Loss Functions for Robust Deep Learning-based Echocardiography Segmentation when Learning with Partially Labelled Data from Multiple Domains

arXiv:2607. 05008v1 Announce Type: cross Abstract: Echocardiography is the first imaging modality used for assessing cardiac function, and accurate segmentation of cardiac structures is essential for deriving biomarkers.

By Iman Islam, Esther Puyol-Ant\'on, Bram Ruijsink, Andrew J. Reader, Andrew P. King

computer-vision

arXiv Machine Learning

Jul 8

Label Hierarchy Transition: Delving into Class Hierarchies to Enhance Deep Classifiers

arXiv:2112. 02353v3 Announce Type: replace-cross Abstract: Hierarchical classification aims to sort the object into a hierarchical structure of categories.

By Renzhen Wang, De cai, Kaiwen Xiao, Xixi Jia, Xiao Han, Deyu Meng

benchmarks

arXiv AI

Jun 8

Which Anatomy Matters Under Limited Labels? A Data-Efficient Anatomy-Aware Benchmark for Cardiac Pathology Prediction

arXiv:2606. 06509v1 Announce Type: cross Abstract: Numerous medical imaging problems must be solved under limited labels and constrained compute, yet it remains unclear whether performance gains are driven mainly by more expressive models or by better representation of clinically meaningful anatomy.

By Himanshu Singh

computer-vision benchmarks

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