arXiv AI

CleanPatrick: A Benchmark for Image Data Cleaning

arXiv:2505. 11034v2 Announce Type: replace-cross Abstract: Robust machine learning depends on clean data, yet current image data cleaning benchmarks rely on synthetic noise or narrow human studies, limiting comparison and real-world relevance.

arXiv Machine Learning

Jul 8

Reliable Mislabel Detection for Video Capsule Endoscopy Data

arXiv:2602. 06938v2 Announce Type: replace-cross Abstract: The classification performance of deep neural networks relies strongly on access to large, accurately annotated datasets.

By Julia Werner, Julius Oexle, Oliver Bause, Maxime Le Floch, Franz Brinkmann, Hannah Tolle, Jochen Hampe, Oliver Bringmann

arXiv Machine Learning

Jul 15

Auditing Data Leakage in Whole-Slide Image Multimodal Benchmarks

arXiv:2607. 12278v1 Announce Type: cross Abstract: Recent vision-language models (VLMs) for computational pathology report striking zero-shot performance on whole-slide image (WSI) visual question answering (VQA) benchmarks.

By Wenhao Zhang, Zhongliang Zhou, John Kang, Sheng Li

llms nlp multimodal benchmarks

arXiv AI

Jul 8

VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection

arXiv:2607. 06254v1 Announce Type: cross Abstract: Deepfake image detection is currently served by three fundamentally different paradigms: commercial APIs, zero-shot vision-language models (LLMs), and open-source detectors.

By Sharayu N. Deshmukh, Md Rashidunnabi, Nelton Tiago Gemo, Kurundkar G. D., Mahamune M. R., Nilesh K. Deshmukh

llms diffusion multimodal benchmarks safety

arXiv Machine Learning

Jun 17

AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active Learning

arXiv:2505. 03509v3 Announce Type: replace Abstract: Anomaly detection in large datasets is essential in astronomy and computer vision.

By Pablo G\'omez, Laslo E. Ruhberg, Maria Teresa Nardone, David O'Ryan

computer-vision 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 2

Text Over Image: Auditing Multimodal Robustness in Synthetic Medical Image Detection

arXiv:2606. 25375v2 Announce Type: replace-cross Abstract: With the rapid adoption of generative AI, synthetic medical images pose growing risks, including diagnostic deception and insurance fraud.

By Ching-Hao Chiu, Hao-Wei Chung, Gelei Xu, Xueyang Li, Pin-Yu Chen, John Kheir, Meysam Ghaffari, Carlos Morato, Ahmed Abbasi, Yiyu Shi

llms multimodal benchmarks

arXiv Machine Learning

Jul 31

DS@GT ARC at ImageCLEFmedical 2026: Architectural Diversity for Concept Detection and Foundation-Model Scaling for Caption Prediction in Medical Image Analysis

arXiv:2607. 27763v1 Announce Type: cross Abstract: We describe the DS@GT submissions to the ImageCLEFmedical Caption 2026 challenge, which continues a long-running benchmark on the ROCOv2 dataset with two tracks: Concept Detection (Task 1), assigning UMLS Concept Unique Identifiers (CUIs) to radiology images, and Caption Prediction (Task 2), generating natural-language captions.

By Bowen Wang, Youwen Zhang, Ritesh Mehta

rag fine-tuning 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

arXiv Machine Learning

Aug 5

Assessment of Conditional Diffusion Model for Synthetic Histopathology Image Generation

arXiv:2608. 03990v1 Announce Type: new Abstract: Synthetic histopathology image generation has emerged as an approach that may address data scarcity in computational pathology, yet current evaluation methodologies may not fully assess synthetic data quality for medical applications.

By Seyed Kahaki, Shijie Li, Weijie Chen, Nicholas Petrick

diffusion computer-vision benchmarks data-engineering

arXiv Machine Learning

Jul 10

A Tool Bottleneck Framework for Clinically-Informed and Interpretable Medical Image Understanding

arXiv:2512. 21414v2 Announce Type: replace-cross Abstract: Recent tool-use frameworks powered by vision-language models (VLMs) improve image understanding by grounding model predictions with specialized tools.

By Christina Liu, Alan Q. Wang, Joy Hsu, Jiajun Wu, Ehsan Adeli

llms multimodal benchmarks

arXiv Machine Learning

Jun 19

EFIQA: Explainable Fundus Image Quality Assessment via Anatomical Priors

arXiv:2606. 20108v1 Announce Type: cross Abstract: Image quality control is vital for a wide range of downstream applications.

By Pengwei Wang, Jos\'e Morano, Qian Wan, Hrvoje Bogunovi\'c

benchmarks

arXiv Machine Learning

Jun 8

An Adaptive Data cleaning Framework for Noisy Label Detection

arXiv:2606. 07086v1 Announce Type: cross Abstract: Deep neural networks (DNNs) excel in computer vision tasks given large annotated datasets.

By Chen-Hsuan Fang, Wei-Hsinag Chen, Pin-Hsuan Yu, Jung-Hua Wang, Tsung-Wei Pan

computer-vision

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