Hugging Face Trending Papers

Layer Selection in VLMs for Zero-Shot OOD Detection via Multi-Resolution Entropy Estimation

The paper investigates zero‑shot out‑of‑distribution (OOD) detection in medical imaging using vision‑language models (VLMs). It shows that intermediate layers, rather than only the final layer, provide valuable OOD signals and that the best layer depends on the imaging modality. To overcome instability in entropy‑based layer selection, the authors introduce a multi‑resolution entropy estimation that aggregates histogram statistics across scales, achieving consistent improvements over state‑of‑the‑art methods on the MIDOG and OASIS benchmarks.

Hugging Face Trending Papers

Jul 23

UnDA: Unpaired Domain Alignment for Cross-Modal Knowledge Transfer in Medical Imaging

Multimodal based approaches often outperform single modality approaches in downstream tasks as the different modalities provide complementary information, yet acquiring paired clinical data remains a significant challenge in real world scenarios. While cross-modal knowledge distillation addresses this, existing methods often struggle with large modality gaps and the propagation of noise from uncertain source-domain predictions.

computer-vision efficiency multimodal safety

arXiv AI

Jul 13

TCLA: Training-Free Class-wise Logit Adaptation for Medical Vision-Language Models

arXiv:2607. 09562v1 Announce Type: cross Abstract: Medical Vision-Language Models (VLMs) exhibit strong zero-shot performance, yet their effectiveness still declines on out-of-distribution (OOD) data due to domain shifts and class bias inherited from large-scale pretraining.

By Tianyou Jiang, Ziyu Zhou

llms 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 AI

Jul 8

Geometry-Aware Uncertainty Coresets for Robust Visual In-Context Learning in Histopathology

arXiv:2605. 18419v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) can couple visual perception with open-ended clinical reasoning, making them attractive for computational histopathology.

By Franciskus Xaverius Erick, Johanna Paula M\"uller, Bernhard Kainz

llms rag fine-tuning efficiency multimodal safety

Hugging Face Trending Papers

Jul 27

Color Fundus Photography Analysis: Co-evolution of Data, Preprocessing, and Modeling toward Multimodal AI

Color Fundus Photography (CFP) is a primary non-invasive imaging modality for large-scale screening of ophthalmic and systemic diseases. Existing surveys mainly summarize task-specific algorithms, datasets, or preprocessing techniques independently, lacking a unified perspective on their co-evolution with modern artificial intelligence.

multimodal

arXiv AI

Jun 8

MMBU: A Massive Multi-modal Biomedical Understanding Benchmark to Probe the Perception Capabilities of Vision-Language Models

arXiv:2606. 06696v1 Announce Type: cross Abstract: Vision and language models (VLMs) hold immense promise to transform biomedical imaging workflows, from detecting lesions in chest X-rays to profiling cellular features in microscopy.

By Ryan D'Cunha, Alejandro Lozano, Xiaoxiao Sun, Daniel Vela Jarquin, Min Woo Sun, Josiah Aklilu, James Burgess, Yuhui Zhang, Ryan Nayebi, Paola Avila, Robayo, Jin Ye, Ming Hu, Zhongying Deng, Junjun He, Xin Chen, Yue Yao, Robert Tibshirani, Jeffrey J. Nirschl, Serena Yeung-Levy

llms computer-vision multimodal benchmarks

Hugging Face Trending Papers

Jul 21

Advancing Multimodal Fusion on Heterogeneous Medical Data with Hybrid Geometry Attention

Multimodal fusion learning (MFL) has shown great potential in the medical domain, where we are faced with disparate data modalities such as imaging, clinical records, and omics. However, existing MFL strategies face several major challenges.

multimodal

arXiv Machine Learning

Jul 27

Autoregressive EHR Foundation Models with Multimodal Inputs

arXiv:2607. 22264v1 Announce Type: new Abstract: Autoregressive foundation models trained on tokenized electronic health records (EHRs) can support zero-shot clinical prediction, yet most operate on structured event codes alone, and do not incorporate multiple modalities in a principled way.

By Yuxuan Liu, Joshua Placidi, Jinpei Han, Alfred John Balston, Marek Rei, A. Aldo Faisal

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 14

Multimodal Routing for Interpretable, Robust, and Auditable Clinical Prediction

arXiv:2607. 09982v1 Announce Type: new Abstract: Electronic health record (EHR) data are inherently multimodal, and leveraging multiple modalities can improve predictive performance.

By Nikkie Hooman, Zhongjie Wu, Eric C. Larson, Mehak Gupta

multimodal safety

arXiv Computation and Language

Aug 21

Towards Clinically Faithful Medical Image Captioning via Enhanced Vision-Language Alignment

arXiv:2608. 19825v1 Announce Type: cross Abstract: Medical image captioning is a technique that accelerates early-stage diagnostic workflows and enhances the interpretability of medical diagnostic AI systems.

By Yunseo Lee, Hyun Jun Kim, Heeseung Shin, Changwon Lim

llms rag multimodal safety data-engineering

arXiv AI

2d ago

ProtoDCS: Towards Robust and Efficient Open-Set Test-Time Adaptation for Vision-Language Models

ProtoDCS introduces a robust open‑set test‑time adaptation framework for vision‑language models, addressing the challenge of simultaneously handling covariate‑shifted in‑distribution (csID) and out‑of‑distribution (csOOD) data. It replaces brittle thresholding with a double‑check separation using a probabilistic Gaussian Mixture Model and employs an evidence‑driven adaptation strategy that updates prototypes efficiently, reducing overconfidence and computational cost. Experiments on CIFAR‑10/100‑C and Tiny‑ImageNet‑C show state‑of‑the‑art performance, improving both known‑class accuracy and OOD detection metrics.

By Wei Luo, Yangfan Ou, Jin Deng, Zeshuai Deng, Xiquan Yan, Zhiquan Wen, Mingkui Tan

llms multimodal benchmarks

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