Tootfinder

Browns signing Quinshon Judkins to rookie contract; second-round RB not expected to play Week 1 https://www.nfl.com/news/browns-signing-quinshon-judkins-to-rookie-contract-second-round-rb-not-expected-to-play-week-1…

Dual-Scale Volume Priors with Wasserstein-Based Consistency for Semi-Supervised Medical Image Segmentation
Junying Meng, Gangxuan Zhou, Jun Liu, Weihong Guo
https://arxiv.org/abs/2509.04273

Dual-Scale Volume Priors with Wasserstein-Based Consistency for Semi-Supervised Medical Image Segmentation
Despite signi cant progress in semi-supervised medical image segmentation, most existing segmentation networks overlook e ective methodological guidance for feature extraction and important prior information from datasets. In this paper, we develop a semi-supervised medical image segmentation framework that e ectively integrates spatial regularization methods and volume priors. Speci cally, our approach integrates a strong explicit volume prior at the image scale and Threshold Dynamics spatia…

Zahl der Kaufinteressenten für den "Cyberbunker" steigt
Rheinland-Pfalz sucht für den sogenannten Cyberbunker einen Käufer. Auch die Bundeswehr interessiert sich für ihren ehemaligen Standort.
ht…

Cowboys cut Super Bowl champion from Eagles after offseason signing https://www.sportingnews.com/us/nfl/dallas-cowboys/news/cowboys-cut-super-bowl-champion-eagles-offseason-signing/5d317b95bb676a01513c0c51

RL-U$^2$Net: A Dual-Branch UNet with Reinforcement Learning-Assisted Multimodal Feature Fusion for Accurate 3D Whole-Heart Segmentation
Jierui Qu, Jianchun Zhao
https://arxiv.org/abs/2508.02557

RL-U$^2$Net: A Dual-Branch UNet with Reinforcement Learning-Assisted Multimodal Feature Fusion for Accurate 3D Whole-Heart Segmentation
Accurate whole-heart segmentation is a critical component in the precise diagnosis and interventional planning of cardiovascular diseases. Integrating complementary information from modalities such as computed tomography (CT) and magnetic resonance imaging (MRI) can significantly enhance segmentation accuracy and robustness. However, existing multi-modal segmentation methods face several limitations: severe spatial inconsistency between modalities hinders effective feature fusion; fusion strate…

GRASPing Anatomy to Improve Pathology Segmentation
Keyi Li, Alexander Jaus, Jens Kleesiek, Rainer Stiefelhagen
https://arxiv.org/abs/2508.03374 https://arx…

GRASPing Anatomy to Improve Pathology Segmentation
Radiologists rely on anatomical understanding to accurately delineate pathologies, yet most current deep learning approaches use pure pattern recognition and ignore the anatomical context in which pathologies develop. To narrow this gap, we introduce GRASP (Guided Representation Alignment for the Segmentation of Pathologies), a modular plug-and-play framework that enhances pathology segmentation models by leveraging existing anatomy segmentation models through pseudolabel integration and featur…

Eagles to sign veteran pass rusher Za'Darius Smith to one-year contract https://www.nfl.com/news/zadarius-smith-eagles-signing

Differential Morphological Profile Neural Networks for Semantic Segmentation
David Huangal, J. Alex Hurt
https://arxiv.org/abs/2509.04268 https://arxiv.org…

Differential Morphological Profile Neural Networks for Semantic Segmentation
Semantic segmentation of overhead remote sensing imagery enables applications in mapping, urban planning, and disaster response. State-of-the-art segmentation networks are typically developed and tuned on ground-perspective photographs and do not directly address remote sensing challenges such as extreme scale variation, foreground-background imbalance, and large image sizes. We explore the incorporation of the differential morphological profile (DMP), a multi-scale shape extraction method base…

Cowboys' All-Pro returner KaVontae Turpin arrested, faces weapon, marijuana charges months after signing deal

https://www.cbssports.com/nfl…

Zero Shot Domain Adaptive Semantic Segmentation by Synthetic Data Generation and Progressive Adaptation
Jun Luo, Zijing Zhao, Yang Liu
https://arxiv.org/abs/2508.03300 https://

Zero Shot Domain Adaptive Semantic Segmentation by Synthetic Data Generation and Progressive Adaptation
Deep learning-based semantic segmentation models achieve impressive results yet remain limited in handling distribution shifts between training and test data. In this paper, we present SDGPA (Synthetic Data Generation and Progressive Adaptation), a novel method that tackles zero-shot domain adaptive semantic segmentation, in which no target images are available, but only a text description of the target domain's style is provided. To compensate for the lack of target domain training data, we ut…