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:2606. 19300v1 Announce Type: cross Abstract: Glioma segmentation in multiparametric MRI is a critical component of treatment planning.
By Xin Ci Wong, Duygu Sarikaya, Kieran Zucker, Marc De Kamps, Nishant Ravikumar
arXiv:2606. 03069v1 Announce Type: cross Abstract: Generalized segmentation of medical images prevents performance degradation when different imaging devices and clinical protocols are used across multiple domains.
By Aqsa Naseer, Maryam Bibi, Syeda Samiya Urooj, Muhammad Khurram Shahzad
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
The paper introduces RABR-Net, a two‑stage framework that refines biomedical image segmentation boundaries by combining multiple uncertainty measures into a boundary‑aware representation. A gated residual refiner uses this representation to selectively correct uncertain boundary pixels while preserving confident regions, leading to modest but statistically significant improvements in Dice, Boundary Dice, and HD95 metrics on a held‑out test set. Qualitative results show the refiner focuses on uncertain cytoplasm and nucleus boundaries, though calibration does not automatically improve.
By Anima Kujur
arXiv:2610.01452v1 Announce Type: new
Abstract: While state-of-the-art automated models for medical image segmentation achieve high mean performance, they frequently suffer from localized, catastroph...
By Samuel Hart, Ahmad Yahya, Ahmed Karam Eldaly