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AMANDA: Agentic Medical Knowledge Augmentation for Data-Efficient Medical Visual Question Answering
Ziqing Wang, Chengsheng Mao, Xiaole Wen, Yuan Luo, Kaize Ding
https://arxiv.org/abs/2510.02328

AMANDA: Agentic Medical Knowledge Augmentation for Data-Efficient Medical Visual Question Answering
Medical Multimodal Large Language Models (Med-MLLMs) have shown great promise in medical visual question answering (Med-VQA). However, when deployed in low-resource settings where abundant labeled data are unavailable, existing Med-MLLMs commonly fail due to their medical reasoning capability bottlenecks: (i) the intrinsic reasoning bottleneck that ignores the details from the medical image; (ii) the extrinsic reasoning bottleneck that fails to incorporate specialized medical knowledge. To addr…

Ein Link ins @… Archiv von Anno Domini 2016:
https://www.heise.de/hintergrund/Anwalt-Mitchell…

MedCAL-Bench: A Comprehensive Benchmark on Cold-Start Active Learning with Foundation Models for Medical Image Analysis
Ning Zhu, Xiaochuan Ma, Shaoting Zhang, Guotai Wang
https://arxiv.org/abs/2508.03441

MedCAL-Bench: A Comprehensive Benchmark on Cold-Start Active Learning with Foundation Models for Medical Image Analysis
Cold-Start Active Learning (CSAL) aims to select informative samples for annotation without prior knowledge, which is important for improving annotation efficiency and model performance under a limited annotation budget in medical image analysis. Most existing CSAL methods rely on Self-Supervised Learning (SSL) on the target dataset for feature extraction, which is inefficient and limited by insufficient feature representation. Recently, pre-trained Foundation Models (FMs) have shown powerful f…

Jaydon Blue injury: Dallas Cowboys standout rookie carted to medical tent with leg injury https://www.si.com/nfl/cowboys/news/jaydon-blue-injury-dallas-cowboys-standout-rookie-carted-to-medical-tent-with-leg-injury…

Tree-of-Reasoning: Towards Complex Medical Diagnosis via Multi-Agent Reasoning with Evidence Tree
Qi Peng, Jialin Cui, Jiayuan Xie, Yi Cai, Qing Li
https://arxiv.org/abs/2508.03038

Tree-of-Reasoning: Towards Complex Medical Diagnosis via Multi-Agent Reasoning with Evidence Tree
Large language models (LLMs) have shown great potential in the medical domain. However, existing models still fall short when faced with complex medical diagnosis task in the real world. This is mainly because they lack sufficient reasoning depth, which leads to information loss or logical jumps when processing a large amount of specialized medical data, leading to diagnostic errors. To address these challenges, we propose Tree-of-Reasoning (ToR), a novel multi-agent framework designed to handl…

Finite 2-group gauge theory and its 3 1D lattice realization
Mo Huang
https://arxiv.org/abs/2508.04693 https://arxiv.org/pdf/2508.04693

Finite 2-group gauge theory and its 3+1D lattice realization
In this work, we employ the Tannaka-Krein reconstruction to compute the quantum double $\mathcal D(\mathcal G)$ of a finite 2-group $\mathcal G$ as a Hopf monoidal category. We also construct a 3+1D lattice model from the Dijkgraaf-Witten TQFT functor for the 2-group $\mathcal G$, generalizing Kitaev's 2+1D quantum double model. Notably, the string-like local operators in this lattice model are shown to form $\mathcal D(\mathcal G)$. Specializing to $\mathcal G = \mathbb{Z}_2$, we demonstrate t…

20 sophomore NFL defenders who could stand out this season -- and what makes each player so special https://www.espn.com/nfl/story/_/id/45907047/2025-nfl-questions-sophomore-defenders-mitchell-dejean-verse-robinson-arnold

Predoc, which uses AI to automate medical records management, raised $30M seed and Series A led by Base10 Partners, with participation from Northzone and others (Allie Garfinkle/Fortune)
https://fortune.com/2025/09/02/predoc-raises-30-million-t…

Predoc raises $30 million to stop document chasing in healthcare
Predoc—which uses AI to automate medical records management—has raised $30 million in funding, Fortune has exclusively learned.

Align Your Query: Representation Alignment for Multimodality Medical Object Detection
Ara Seo, Bryan Sangwoo Kim, Hyungjin Chung, Jong Chul Ye
https://arxiv.org/abs/2510.02789 h…

Align Your Query: Representation Alignment for Multimodality Medical Object Detection
Medical object detection suffers when a single detector is trained on mixed medical modalities (e.g., CXR, CT, MRI) due to heterogeneous statistics and disjoint representation spaces. To address this challenge, we turn to representation alignment, an approach that has proven effective for bringing features from different sources into a shared space. Specifically, we target the representations of DETR-style object queries and propose a simple, detector-agnostic framework to align them with modal…

Med-K2N: Flexible K-to-N Modality Translation for Medical Image Synthesis
Feng Yuan, Yifan Gao, Yuehua Ye, Haoyue Li, Xin Gao
https://arxiv.org/abs/2510.02815 https://

Med-K2N: Flexible K-to-N Modality Translation for Medical Image Synthesis
Cross-modal medical image synthesis research focuses on reconstructing missing imaging modalities from available ones to support clinical diagnosis. Driven by clinical necessities for flexible modality reconstruction, we explore K to N medical generation, where three critical challenges emerge: How can we model the heterogeneous contributions of different modalities to various target tasks? How can we ensure fusion quality control to prevent degradation from noisy information? How can we mainta…