Tootfinder

Heute bin ich mir sozusagen selber aufs Dach gestiegen: der letzte Sturm hatte einen Dachziegel ausgehebelt, der Ziegel ist dann einen halben Meter das Dach runter gerutscht und auf den anderen Ziegeln liegen geblieben. Jetzt ist der Dachziegel wieder an Ort und Stelle. Never take broken for an answer. #diy

Raiders Mailbag: Geno Smith's Silver and Black debut, potential roster additions and more questions answered https://www.raiders.com/news/mailbag/raiders-mailbag-geno-smith-debut-potential-additions-and-more-questions-answer…

Raiders Mailbag: Geno Smith's Silver and Black debut, potential roster additions and more questions answered https://www.raiders.com/news/mailbag/raiders-mailbag-geno-smith-debut-potential-additions-and-more-questions-answer…

EZhouNet:A framework based on graph neural network and anchor interval for the respiratory sound event detection
Yun Chu, Qiuhao Wang, Enze Zhou, Qian Liu, Gang Zheng
https://arxiv.org/abs/2509.01153

EZhouNet:A framework based on graph neural network and anchor interval for the respiratory sound event detection
Auscultation is a key method for early diagnosis of respiratory and pulmonary diseases, relying on skilled healthcare professionals. However, the process is often subjective, with variability between experts. As a result, numerous deep learning-based automatic classification methods have emerged, most of which focus on respiratory sound classification. In contrast, research on respiratory sound event detection remains limited. Existing sound event detection methods typically rely on frame-level…

Medico 2025: Visual Question Answering for Gastrointestinal Imaging
Sushant Gautam, Vajira Thambawita, Michael Riegler, P{\aa}l Halvorsen, Steven Hicks
https://arxiv.org/abs/2508.10869

Medico 2025: Visual Question Answering for Gastrointestinal Imaging
The Medico 2025 challenge addresses Visual Question Answering (VQA) for Gastrointestinal (GI) imaging, organized as part of the MediaEval task series. The challenge focuses on developing Explainable Artificial Intelligence (XAI) models that answer clinically relevant questions based on GI endoscopy images while providing interpretable justifications aligned with medical reasoning. It introduces two subtasks: (1) answering diverse types of visual questions using the Kvasir-VQA-x1 dataset, and (2…

Querying GI Endoscopy Images: A VQA Approach
Gaurav Parajuli
https://arxiv.org/abs/2507.21165 https://arxiv.org/pdf/2507.21165

Querying GI Endoscopy Images: A VQA Approach
VQA (Visual Question Answering) combines Natural Language Processing (NLP) with image understanding to answer questions about a given image. It has enormous potential for the development of medical diagnostic AI systems. Such a system can help clinicians diagnose gastro-intestinal (GI) diseases accurately and efficiently. Although many of the multimodal LLMs available today have excellent VQA capabilities in the general domain, they perform very poorly for VQA tasks in specialized domains such …

Communicating Smartly in Molecular Communication Environments: Neural Networks in the Internet of Bio-Nano Things
Jorge Torres G\'omez, Pit Hofmann, Lisa Y. Debus, Osman Tugay Ba\c{s}aran, Sebastian Lotter, Roya Khanzadeh, Stefan Angerbauer, Bige Deniz Unluturk, Sergi Abadal, Werner Haselmayr, Frank H. P. Fitzek, Robert Schober, Falko Dressler
ht…

Communicating Smartly in Molecular Communication Environments: Neural Networks in the Internet of Bio-Nano Things
Recent developments in the Internet of Bio-Nano Things (IoBNT) are laying the groundwork for innovative applications across the healthcare sector. Nanodevices designed to operate within the body, managed remotely via the internet, are envisioned to promptly detect and actuate on potential diseases. In this vision, an inherent challenge arises due to the limited capabilities of individual nanosensors; specifically, nanosensors must communicate with one another to collaborate as a cluster. Aiming…

This https://arxiv.org/abs/2505.22240 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

BioHopR: A Benchmark for Multi-Hop, Multi-Answer Reasoning in Biomedical Domain
Biomedical reasoning often requires traversing interconnected relationships across entities such as drugs, diseases, and proteins. Despite the increasing prominence of large language models (LLMs), existing benchmarks lack the ability to evaluate multi-hop reasoning in the biomedical domain, particularly for queries involving one-to-many and many-to-many relationships. This gap leaves the critical challenges of biomedical multi-hop reasoning underexplored. To address this, we introduce BioHopR,…

CF-Seg: Counterfactuals meet Segmentation
Raghav Mehta, Fabio De Sousa Ribeiro, Tian Xia, Melanie Roschewitz, Ainkaran Santhirasekaram, Dominic C. Marshall, Ben Glocker
https://arxiv.org/abs/2506.16213

CF-Seg: Counterfactuals meet Segmentation
Segmenting anatomical structures in medical images plays an important role in the quantitative assessment of various diseases. However, accurate segmentation becomes significantly more challenging in the presence of disease. Disease patterns can alter the appearance of surrounding healthy tissues, introduce ambiguous boundaries, or even obscure critical anatomical structures. As such, segmentation models trained on real-world datasets may struggle to provide good anatomical segmentation, leadin…