arXiv AI July 14, 2026 By Yash Shah, Omar Todd, Philipp Seeb\"ock, Georg Langs, Ben Glocker, Raghav Mehta

GRC-ProbNet: Uncertainty-aware Feature Extraction for Cardiovascular Disease Classification

arXiv:2607. 10357v1 Announce Type: cross Abstract: The automatic detection and classification of cardiovascular disease (CVD) from computed tomography (CT) images plays an important role in clinical practice.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

computer-vision

arXiv Machine Learning

Jul 27

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography

arXiv:2607. 22139v1 Announce Type: cross Abstract: Accurate pixel-level classification of coronary angiograms is critical for cardiovascular disease assessment, yet the field lacks standardized evaluation protocols.

By Dominik Bernard Lau, Hubert Malinowski, Jerzy Szyjut, Adam Brzeski, Tomasz Dziubich, Rados{\l}aw Targo\'nski, Tomasz Figatowski, Natalia Zieli\'nska

computer-vision benchmarks

arXiv AI

Jul 14

A Unified Framework for Comprehensive Cardiac CT Segmentation and Phenotyping: Human-in-the-Loop Data Annotation, Vision Foundation Model Development, Multicenter Evaluation and Clinical Validation

arXiv:2607. 11287v1 Announce Type: cross Abstract: Comprehensive quantification of cardiac structures from computed tomography (CT) remains limited not by data availability but by the scalability of measurements, which makes routine use impractical.

By Pooya Mohammadi Kazaj, Leo Fridolin Weber, Wen Xie, Seyed Amir Ahmad Safavi-Naini, Anselm Stark, Giovanni Baj, Ali Mokhtari, Toshiya Yoshida, Christoph Ryffel, Taishi Okuno, Yoshihiro Akashi, Ronny R. Buechel, Thomas Pilgrim, Waldo Valenzuela, George C. M. Siontis, Xiaowei Xu, Moritz Hundertmark, Stephan Windecker, Christoph Grani, Isaac Shiri

llms computer-vision benchmarks data-engineering

arXiv Machine Learning

1d ago

Beyond Boundary Noise: Aggregated Aleatoric Uncertainty Fails to Capture Presence Ambiguity in 3D Lung Nodule Segmentation

arXiv:2608. 14766v1 Announce Type: cross Abstract: Uncertainty estimation is critical for the safe clinical deployment of deep learning in medical image segmentation, with aleatoric uncertainty theoretically designed to capture irreducible data ambiguity.

By Simon Baur, Arne Schernich, Ekin B\"oke, Wojciech Samek, Jackie Ma

computer-vision safety

arXiv Machine Learning

Jun 16

Learning a Sampling-Free Variational DNN Plugin from Tiny Training Sets to Refine OOD Segmentation With Uncertainty Estimation

arXiv:2606. 15837v1 Announce Type: cross Abstract: Deep neural networks (DNNs) frequently fail to generalize to out-of-distribution (OOD) medical images because of variations in scanners and acquisition protocols.

By Jimut B. Pal, Suyash P. Awate

computer-vision

arXiv AI

Jul 8

Decoupled Single-Mask Annotation Noise Detection via Cross-Sectional Patch Self-Consistency

arXiv:2607. 05965v1 Announce Type: cross Abstract: Vascular computed tomography datasets are commonly annotated only once per scan, yielding the pervasive yet under addressed problem of single mask annotation noise.

By Yinheng Zhu, Xiaowei Xu

rag

arXiv Machine Learning

Jul 22

Tumor-anchored deep feature random forests for out-of-distribution detection in lung cancer segmentation

arXiv:2512. 08216v4 Announce Type: replace-cross Abstract: Accurate segmentation of lung tumors from 3D computed tomography (CT) scans is essential for automated treatment planning and response assessment.

By Aneesh Rangnekar, Harini Veeraraghavan

llms computer-vision benchmarks safety

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CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography

A Unified Framework for Comprehensive Cardiac CT Segmentation and Phenotyping: Human-in-the-Loop Data Annotation, Vision Foundation Model Development, Multicenter Evaluation and Clinical Validation

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Learning a Sampling-Free Variational DNN Plugin from Tiny Training Sets to Refine OOD Segmentation With Uncertainty Estimation

Decoupled Single-Mask Annotation Noise Detection via Cross-Sectional Patch Self-Consistency

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