arXiv AI By Vincenzo Marcian\`o, Xiaoming Zhang, Gianluca Guglielmo, Sebastien Ourselin, Michela Antonelli, Maria A. Zuluaga

MID: Mask-Image Distributional Divergence for Evaluating Medical Image Segmentation

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arXiv Machine Learning
Jul 28

Trustworthy Medical Segmentation: Uncertainty-Aware U-Net Evaluation Under Clinical Image Degradation

arXiv:2607. 22727v1 Announce Type: cross Abstract: Medical image segmentation models often report high benchmark accuracy under ideal imaging conditions, yet their failures under clinical degradation can be quiet: sensor noise, patient motion, low- resolution acquisition, and contrast variability may all alter model behavior without producing an obvious warning.

By Pranav Kaliaperumal, Manisha Kaliaperumal
arXiv Machine Learning
Aug 5

Assessment of Conditional Diffusion Model for Synthetic Histopathology Image Generation

arXiv:2608. 03990v1 Announce Type: new Abstract: Synthetic histopathology image generation has emerged as an approach that may address data scarcity in computational pathology, yet current evaluation methodologies may not fully assess synthetic data quality for medical applications.

By Seyed Kahaki, Shijie Li, Weijie Chen, Nicholas Petrick
arXiv AI
Jun 2

Pre-Deployment Robustness Stress Testing for CT Segmentation Systems Using Clinically Motivated Multi-Corruption Augmentation

arXiv:2606. 00491v1 Announce Type: cross Abstract: Deep learning-based CT segmentation systems often achieve high accuracy on clean benchmark images, but their performance may degrade under heterogeneous clinical imaging conditions such as noise, resolution loss, contrast variation, intensity shift, and artifacts.

By CholMin Kang, Jonghyun Chung, Amanpreet Kaurb, Nagesh Gulkotwarb, Arthi Sivasankaranb
arXiv Machine Learning
Aug 18

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