arXiv AI

Reducing Redundancy in Whole-Slide Image Patching for Scalable Indexing and Retrieval

arXiv:2606. 26157v1 Announce Type: cross Abstract: The rapid growth of digital pathology has created an urgent need for efficient indexing and retrieval of whole slide images (WSIs).

arXiv AI

Jun 2

CRISP -- Clustering-Based Redundancy-Reduced Instance Sampling for Pathology Case Representation and Retrieval

arXiv:2605. 24253v2 Announce Type: replace-cross Abstract: Digital pathology archives increasingly contain multiple whole-slide images (WSIs) per case, capturing spatially distinct tumour regions and reflecting intrinsic morphological heterogeneity.

By Zahra Rahimi Afzal, Wataru Uegami, Saghir Alfasly, Saba Yasir, Judy C. Boughey, Matthew P. Goetz, Krishna R. Kalari, H. R. Tizhoosh

agents

arXiv Computer Vision

Sep 21

MAGIC: Marginal-Guided Compression with Optimal Transport for Efficient Visual Document Retrieval

arXiv:2609.21018v1 Announce Type: new Abstract: Recent visual document retrieval (VDR) systems such as ColPali use multi-vector page embeddings, in which patch-level vectors enable fine-grained evide...

By Xu Yuan, Hua Liu, Wenqi Fan, Qing Li

rag benchmarks

arXiv Computer Vision

Sep 15

Accurate and Scalable Multimodal Pathology Retrieval via Attentive Vision-Language Alignment

arXiv:2510.23224v2 Announce Type: replace Abstract: The rapid digitization of histopathology slides has opened new opportunities for computational tools in clinical and research workflows. Content-ba...

By Hongyi Wang, Zhengjie Zhu, Junlin Hou, Jiabo Ma, Fang Wang, Yue Shi, Qiuyu Cai, Jili Wang, Bo Luo, Zhizhong Chai, Zhengyu Zhang, Li Liang, Xiuming Zhang, Yen-Wei Chen, Lanfen Lin, Hao Chen

rag multimodal safety

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

Jun 10

++nnU-Net: Scaling nnU-Net with Prefix-Based Data Augmentation

arXiv:2606. 10713v1 Announce Type: cross Abstract: The nnU-Net has demonstrated continuous success in medical segmentation tasks, which heavily rely on the availability and diversity of annotated biomedical data.

By Ana Sofia Santos, Andr\'e Ferreira, Gijs Luijten, Naida Solak, Lisle Faray de Paiva, Behrus Hinrichs-Puladi, Jens Kleesiek, Jan Egger, Victor Alves

computer-vision safety

Hugging Face Trending Papers

Jul 30

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

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. For Task 1, our primary submission was a three-way late-fusion ensemble of ConvNeXt-V2, BiomedCLIP ViT-B/16, and DenseNet-169 with a regularized ''Honest Threshold Tuning'' procedure designed to avoid validation overfitting on rare concepts; this submission ranked first on the official submission with a primary $F_1$ of $0.

rag fine-tuning benchmarks

arXiv AI

Sep 15

Iterative Multimodal Retrieval-Augmented Generation for Medical Question Answering

arXiv:2604.27724v2 Announce Type: replace Abstract: Medical retrieval-augmented generation (RAG) systems typically operate on text chunks extracted from biomedical literature, discarding the rich vis...

By Xupeng Chen, Binbin Shi, Chenqian Le, Jiaqi Zhang, Kewen Wang, Ran Gong, Jinhan Zhang, Chihang Wang

llms rag computer-vision nlp multimodal benchmarks

arXiv AI

Jun 26

Generative Retrieval via Diffusion Transformer with Metric-Ordered Sequence Training and Hybrid-Policy Preference Optimization

arXiv:2606. 26899v1 Announce Type: new Abstract: Embedding-based retrieval ranks items by their similarity to a query in a shared vector space and usually aims to return the highest-scoring items.

By Chenghao Liu, Yu Zhang, Zhongtao Jiang, Kun Xu, Zhenwei An, Renzhi Wang, Zhao Wang, Jiachen Zhang, Yuxiao Zhang, Kun Xu, Songfang Huang

llms rag diffusion fine-tuning benchmarks

arXiv AI

Aug 11

Simple Token-Efficient Vision-Language Model for Case-level Pathology Synoptic Report Generation

arXiv:2605. 30716v2 Announce Type: replace-cross Abstract: Generating clinically useful pathology reports for pathology cases from whole-slide images (WSIs) is challenging due to gigapixel resolution, long visual-token sequences, and the complexity of case-level reasoning, where a single case may contain multiple WSIs with heterogeneous tissues and ambiguous findings.

By Zhiyuan Yang, Jiahao Cheng, Vincent Quoc-Huy Trinh, Mahdi S. Hosseini

llms fine-tuning multimodal

Hugging Face Trending Papers

Jun 25

Generative Retrieval via Diffusion Transformer with Metric-Ordered Sequence Training and Hybrid-Policy Preference Optimization

Embedding-based retrieval ranks items by their similarity to a query in a shared vector space and usually aims to return the highest-scoring items. In many production settings this is not what is wanted: given a seed set that expresses a fine-grained pattern, one needs more items that both satisfy a target attribute and stay within that pattern.

llms rag diffusion fine-tuning 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 Machine Learning

Jun 30

The Voronoi Bottleneck: Capacity-Aware Dense Retrieval for Product Search

arXiv:2606. 28359v1 Announce Type: cross Abstract: Dense embedding retrieval compresses all relevance information into a single inner product, imposing a fundamental geometric limit -- the Voronoi Bottleneck -- on the number of query-document relevance patterns expressible at fixed embedding dimension (d).

By Charith Chandra Sai Balne, Rithwik Maramraju, Siddharth Pratap Singh, Rohit Upadhyay, Aditya Singh, Chittaranjan Tripathy, Yogananda Domlur Seetharama

rag

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