Hugging Face Trending Papers July 15, 2026

Beyond scalar losses: calibrating segmentation models via gradient vector field surgery

Read the original on Hugging Face Trending Papers →

Region-based loss functions, such as the Dice loss, have established themselves as the de facto standard for highly class- and region-imbalanced segmentation tasks. However, models trained using region-based loss functions are notoriously miscalibrated and typically yield over-confident predictions.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

computer-vision

arXiv AI

Jul 17

Beyond scalar losses: calibrating segmentation models via gradient vector field surgery

arXiv:2607. 14338v1 Announce Type: cross Abstract: Region-based loss functions, such as the Dice loss, have established themselves as the de facto standard for highly class- and region-imbalanced segmentation tasks.

By Laurin Lux, Alexander H. Berger, Moritz Knolle, Daniel R\"uckert, Johannes C. Paetzold

computer-vision

Hugging Face Trending Papers

Jun 22

Polynomial Dice Loss for Medical Image Segmentation

Medical image segmentation is a fundamental task for medical image processing and computer-assisted intervention, yet data imbalance and small lesion detection pose significant challenges. Dice Loss, which measures the overlap between predicted and ground truth regions, is widely used to mitigate these issues.

computer-vision

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

computer-vision multimodal benchmarks safety

arXiv AI

Jun 12

Improving Pre-trained Adult Glioma Segmentation Models Using only Post-processing Techniques

arXiv:2512. 14937v2 Announce Type: replace-cross Abstract: Gliomas are the most common malignant brain tumors in adults and are among the most lethal.

By Abhijeet Parida, Daniel Capell\'an-Mart\'in, Zhifan Jiang, Nishad Kulkarni, Krithika Iyer, Austin Tapp, Syed Muhammad Anwar, Mar\'ia J. Ledesma-Carbayo, Marius George Linguraru

computer-vision

arXiv AI

Jun 3

ROBUST-WT: Robust Uncertainty-aware Segmentation Transform via Whitening and Training Enhancements

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

computer-vision efficiency benchmarks

arXiv AI

Jun 16

Federated Medical Image Segmentation under Real-World Label Noise: A Benchmark Suite for Noisy Label Learning Method Selection

arXiv:2606. 16868v1 Announce Type: cross Abstract: While federated learning (FL) enables collaborative medical image segmentation without centralizing sensitive data, real-world deployment is frequently complicated by cross-site label imperfections such as contour disagreement, missing or additional structures, and confused labels.

By Markus Bujotzek, Dimitrios Bounias, Stefan Denner, Ralf Floca, Maximilian Fischer, Peter Neher, Klaus Maier-Hein

computer-vision benchmarks

Related stories

Beyond scalar losses: calibrating segmentation models via gradient vector field surgery

Polynomial Dice Loss for Medical Image Segmentation

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

Improving Pre-trained Adult Glioma Segmentation Models Using only Post-processing Techniques

ROBUST-WT: Robust Uncertainty-aware Segmentation Transform via Whitening and Training Enhancements

Federated Medical Image Segmentation under Real-World Label Noise: A Benchmark Suite for Noisy Label Learning Method Selection