Hugging Face Trending Papers

Beyond Accuracy: Quantifying Pulmonary Attribution in Anatomy-Guided Chest X-Ray Classification Under Domain Shift

arXiv Computer Vision

3d ago

Beyond Accuracy: Quantifying Pulmonary Attribution in Anatomy-Guided Chest X-Ray Classification Under Domain Shift

arXiv:2608.30467v1 Announce Type: new Abstract: Deep-learning models can achieve strong chest X-ray (CXR) classification performance without establishing whether their predictions predominantly rely...

By Abdullah Al Mamun, Md. Nasif Osman Khansur, Md Ashraful Hossen Akash, Md. Kishor Morol, Tze Hui Liew

llms

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 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

arXiv Computer Vision

Aug 25

Extending the Horizon of Early Diagnosis: Lung Cancer Prediction with Vision Transformers

arXiv:2608.21571v1 Announce Type: new Abstract: Lung cancer remains a leading cause of cancer-related mortality worldwide, and early diagnosis is critical for improving survival. However, early-stage...

By Olivera Kotevska, Ian Goethert, Michael McGee, Maria Mahbub, Sean R. Wilkinson, Rowena Yip, Myvizhi Esai Selvan, Zeynep H. Gumus, Claudia Henschke, Robert J. Klein, Providencia Morales, Samuel M Aguayo, Ioana Danciu, Mayanka Chandrashekar

llms fine-tuning

arXiv AI

3d ago

Extending TotalSegmentator: Predicting Patient and Acquisition Characteristics from CT and MR Images

arXiv:2608.29348v1 Announce Type: new Abstract: Background: Patient details and acquisition metadata are important for clinical decisions, image quality control, and automated research pipelines, but...

By Jakob Wasserthal, Joshy Cyriac, Michael Bach, Kimia Mozahheb Yousefi, Minh-Son To, M\'at\'e Sik, C\'edric H\'emon, Thomas Weikert, Martin Segeroth

computer-vision

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 Computer Vision

4d ago

ARC-CT: Anatomy-Routed Contrastive Vision-Language Learning for 3D Chest CT

arXiv:2608.28455v1 Announce Type: new Abstract: Contrastive vision-language learning uses paired chest CT volumes and radiology reports to learn abnormality classifiers without manually annotated lab...

By Huseyin Umut Isik, Mehmet Alp Ozaydin, Sila Kurugol, \c{S}eyda Ertekin

llms rag multimodal safety

arXiv Machine Learning

Aug 13

Uncertainty-Aware Compositional Localization and Placement Assessment of Catheters and Tubes in Chest X-Rays

arXiv:2608. 11288v1 Announce Type: cross Abstract: Assessing catheter and tube placement on chest X-rays is safety-critical yet tedious and error-prone.

By Harshil Lodhiya

safety

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

Aug 18

MIRROR: Multimodal Intelligent Radiology Reasoning and Observation Reporter

arXiv:2608. 16709v1 Announce Type: cross Abstract: A radiologist reading a model's output faces two problems.

By Vignesh Nagarajan, Sriram Venkatapathy

multimodal

arXiv AI

Aug 11

Distilling CT Foundation Models into Editable Concept Bottlenecks for Lung Nodule Malignancy Prediction

arXiv:2608. 07857v1 Announce Type: cross Abstract: Foundation models provide transferable CT representations, but predictions based directly on these embeddings are difficult to interpret.

By Fakrul Islam Tushar, Stephen Adamo, Geoffrey D. Rubin

rag

arXiv Computer Vision

4d ago

A Controlled Audit of Architectural Complexity in Uncertainty-Aware Multi-Organ Ultrasound Classification

arXiv:2608. 28063v1 Announce Type: new Abstract: Multi-organ ultrasound classifiers increasingly combine attention, mixture-of-experts routing, uncertainty gating, and evidential deep learning (EDL) objectives to address heterogeneous anatomy and acquisition.

By Yang Song, Pengbo Sun, Shichang Feng, Ye Zhu, Xin Xu, Ziran Wang

safety

Related stories