arXiv Machine Learning

Tokenizer Generator Coupling in Medical Image Generation

arXiv:2608. 07713v1 Announce Type: cross Abstract: Latent medical image generators usually treat the tokenizer as fixed preprocessing.

arXiv Machine Learning

Jun 9

Assessing Sample Quality in Conditional Generation under Compositional Shift

arXiv:2606. 09601v1 Announce Type: new Abstract: Conditional generators provide a natural tool for controllable generation, including settings where the desired condition is a new composition of observed attributes or experimental factors.

By Berker Demirel, Valentino Maiorca, Marco Fumero, Theofanis Karaletsos, Francesco Locatello

benchmarks

arXiv AI

Jul 23

Self-supervision drives representational convergence in medical foundation models more than clinical supervision

arXiv:2607. 20274v1 Announce Type: cross Abstract: Medical image encoders from different groups are increasingly treated as interchangeable, on the assumption that scale and clinical supervision concentrate their representations onto a shared structure.

By Soroosh Tayebi Arasteh, Sebastian Ziegelmayer, Mahshad Lotfinia, Lisa Adams, Sven Nebelung, Jakob Nikolas Kather, Daniel Truhn

multimodal

arXiv AI

Jul 17

Demographically-Conditioned Synthetic Medical Images for Bias Mitigation and Bias Detection in Disease Classifiers

arXiv:2607. 14984v1 Announce Type: new Abstract: Per-subgroup fairness audits of medical image classifiers face a sample-size problem: minority subgroups in held-out test sets have so few samples that the resulting confidence intervals on per-subgroup performance are wider than the bias the audit is meant to detect.

By Mahmoud Ibrahim, Bart Elen, Chang Sun, Gokhan Ertaylan, Michel Dumontier

diffusion fine-tuning safety

arXiv AI

Jun 2

Cross-modal linkage risk in clinical vision-language models

arXiv:2606. 02276v1 Announce Type: cross Abstract: Vision-language models (VLMs) trained on paired chest radiographs and radiology reports learn a shared embedding space that can preserve instance-level image-report correspondence.

By Soroosh Tayebi Arasteh, Mahshad Lotfinia, Sven Nebelung, Daniel Truhn

llms rag fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Jul 9

MedPMC: A Systematic Framework for Scaling High-Fidelity Medical Multimodal Data for Foundation Models

arXiv:2607. 07673v1 Announce Type: cross Abstract: Medicine is inherently multimodal, requiring clinicians to synthesize information across diverse data streams.

By Hyunjae Kim, Dain Kim, Pan Xiao, Serina S. Applebaum, Younjoon Chung, Xuguang Ai, Yu Yin, Roy Jiang, Yuexi Du, Yawen Wei, Yiming Kong, Tuo Guo, Zhiyuan Cao, Mengmeng Du, Yuelei Fu, Yan Hu, Rui Shi, Gui Yang, Kevin W. Jin, Yuntian Liu, Yuxuan Tian, Jonathan Marquez, Zhen Chen, Sheng Zhang, Hoifung Poon, Hua Xu, Jaewoo Kang, Qingyu Chen

llms multimodal benchmarks safety

arXiv Machine Learning

Jul 20

qZACH-ViT: Quantization-Aware Intrinsic Explanations with Recursive Attribution-Stabilized Optimization

arXiv:2607. 15421v1 Announce Type: new Abstract: Compact medical-image classifiers need efficiency and interpretable evidence, yet these goals are often addressed separately.

By Athanasios Angelakis

efficiency

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 Machine Learning

Aug 4

What EEG Foundation Models Encode: Dataset Identity and a Negative-Control Suite for Clinical Benchmarks

arXiv:2607. 24519v2 Announce Type: replace Abstract: Pretrained EEG foundation models are proposed for clinical decoding, but whether reported gains transfer across populations or survive negative controls is unclear.

By Marzieh Zare

rag fine-tuning benchmarks

arXiv Machine Learning

Aug 12

Predictive Entropy as a Joint Screen for Error and Paraphrase Instability in Medical Vision-Language Models

arXiv:2604. 08941v2 Announce Type: replace Abstract: Medical Vision-Language Models (VLMs) answering binary presence questions on chest radiographs can fail in two linked ways: they are confidently wrong, and they change answers when a clinically equivalent question is rephrased.

By Binesh Sadanandan, Vahid Behzadan

llms multimodal

arXiv AI

Aug 5

CARE-X: Towards Clinically Useful Radiology VLMs with Auxiliary Supervision, Reward-Aligned Learning, and Tool-Augmented Measurement

arXiv:2608. 03890v1 Announce Type: cross Abstract: A clinically useful chest X-ray system must go beyond fluent report generation: it should classify findings with tunable decision thresholds, localize them spatially, and derive the anatomical measurements upon which many diagnoses depend.

By Mercy Prasanna Ranjit, Anirban Porya, Sathvik Joel, Niharika Vadlamudi, Nikhilesh Chowdary Eathamukkala, Prasanth V V, Abhyuday Kumara Swamy, Pranay Narhari Umredkar, Pradeep Narayan, Vivek Rajagopal, Tanuja Ganu

llms nlp multimodal benchmarks

arXiv AI

Aug 11

JustLLMGRPO: Radiographic Control for Chest X-Ray Generation

arXiv:2608. 08046v1 Announce Type: new Abstract: Text-conditioned chest X-ray generation aims to synthesize realistic radiographs that faithfully depict specified findings.

By Pengxiang Cai, Xiaohan Li, Anglin Liu, Qingyuan Zeng, Zexun Li, Jintai Chen

llms fine-tuning benchmarks safety

arXiv AI

Jun 10

A Controlled Audit of Pretraining Contamination in Public Medical Vision-Language Benchmarks

arXiv:2606. 10066v1 Announce Type: cross Abstract: Medical vision-language models (VLMs) are evaluated on public benchmarks whose images and question-answer pairs have been freely downloadable for years, yet reported accuracy assumes these examples were absent from pretraining.

By Bruce Changlong Xu, Lan Wu, Alexander Ryu

llms multimodal benchmarks

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