arXiv Computer Vision September 30, 2026 By Haoyan Ding, Kritika Iyer, Halid Yerebakan, Zhenyu Bu, Chushu Shen, Peiyu Duan, Xinyuan Zheng, Sepehr Farhand, Xueqi Guo, Chaowei Wu, Yoshihisa Shinagawa, Gerardo Hermosillo Valadez

FD-AA: A Lightweight Focal-Diffuse And Attenuation-Aware Head for Incidental Abdominal Abnormality Detection in Chest CT

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benchmarks

arXiv Computer Vision

Aug 31

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 Computer Vision

Aug 26

Example-based Robust Abnormality Detection with Minimal Annotations using Exemplar Med-DETR

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...

By Sheethal Bhat, Bogdan Georgescu, Awais Mansoor, Mathias Zinnen, Pranjal Sahu, Florin C. Ghesu, Sasa Grbic, Andreas Maier

computer-vision multimodal benchmarks

arXiv Computer Vision

Sep 1

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 Computer Vision

Sep 10

Federated Learning for Cross-Modality Medical Image Segmentation via Augmentation-Driven Generalization

arXiv:2602.20773v2 Announce Type: replace Abstract: Purpose: Developing generalizable medical image segmentation models is challenging because imaging data are distributed across institutions and dif...

By Sachin Dudda Nagaraju, Ashkan Moradi, Bendik Skarre Abrahamsen, Mattijs Elschot

computer-vision multimodal

arXiv Machine Learning

Jun 16

A Multi-Center Benchmark for Abdominal Disease Diagnosis and Report Generation from Non-Contrast CT

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.

By Mariam Elbakry, Aliaa Sayed Sheha, Salma Hassan Tantawy, Aya Yassin, Concetto Spampinato, Karim Lekadir, Xiaomeng Li, Marawan Elbatel

multimodal benchmarks

arXiv AI

Jun 30

Towards Modality-Agnostic Medical Image Anomaly Detection: A Training-Free Manifold Refinement Approach

arXiv:2604. 19191v2 Announce Type: replace-cross Abstract: Deploying AI-based anomaly detection across diverse clinical imaging settings remains challenging because most existing methods rely on modality-specific architectures, anatomical priors, or extensive retraining, limiting their use as general-purpose screening tools.

By Pritam Kar, Gouri Lakshmi S, Saptarshi Bej

rag diffusion benchmarks

One email a morning, machine-written

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

Example-based Robust Abnormality Detection with Minimal Annotations using Exemplar Med-DETR

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

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Towards Modality-Agnostic Medical Image Anomaly Detection: A Training-Free Manifold Refinement Approach