arXiv Computer Vision
Multi-Stage Prompt-Guided Feature Modulation for Generalizable Brain Tumor Segmentation
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arXiv Computer Vision
Aug 27
Synergistic Modality-and-Slice Memory Framework for Cross-Modal 3D Brain Tumor Segmentation
The paper introduces MSM‑Seg, a dual‑memory segmentation framework for 3D multi‑modal brain tumor segmentation. It combines a modality‑and‑slice memory attention module to capture cross‑modal and spatial‑slice dependencies, a multi‑scale category‑agnostic prompt encoder for whole‑tumor guidance, and a modality‑adaptive fusion decoder to integrate complementary decoding information. Experiments on various MRI datasets show that MSM‑Seg surpasses state‑of‑the‑art methods for metastases and glioma tumor segmentation.
arXiv Computer Vision
Sep 3
Generalizable Brain Tumor Segmentation with Self-Training and Tumor-Aware Deformations
The paper introduces a method for generalizable brain tumor segmentation in the BraTS 2026 Challenge. It builds on the nnU-Net framework with a large residual encoder, adding semi‑supervised learning via pseudo‑labels and a tumor‑aware deformable augmentation that locally deforms lesions while preserving surrounding anatomy. The approach improves Dice and NSD scores across all tumor regions compared to labeled‑only baselines, demonstrating the complementary benefits of self‑training and the proposed augmentation.
arXiv AI
Aug 21
Prompt-Conditioned Channel Attention for Hierarchical Feature Modulation toward Anatomy-Agnostic Segmentation
arXiv:2608. 20229v1 Announce Type: cross Abstract: Anatomically plausible segmentation remains challenging because of low contrast, ambiguous boundaries, and modality-specific artifacts.
arXiv Machine Learning
Sep 16
NeuroTS-Net: Multi-Class Semantic Segmentation of Pediatric Brain Tumors in Multi-Modal MRI
NeuroTS-Net is a 3‑D encoder‑decoder CNN designed for multi‑class semantic segmentation of pediatric brain tumors in multi‑modal MRI. It uses a dual‑scale raw‑detail stream, adaptive low‑resolution context selection, and detail‑preserving multipath downsampling to maintain fine intensity and boundary information while modeling broader tumor context. Trained on the BraTS 2026 pediatric dataset, it outperformed nnU‑Net and MedNeXt, achieving Dice scores of 0.938/0.937 on internal validation and 0.927/0.926 on the official challenge set.
arXiv AI
Aug 28
Pixel Wised Lesion Prediction on COVID-19 CT Imagery: A Comparative Analysis of Automated Image Segmentation Architectures
The study evaluates four deep‑learning segmentation architectures—Unet, PSPNet, Linknet, and FPN—paired with six pre‑trained encoders to predict COVID‑19 lesions in CT images. Experiments on three COVID‑19 CT datasets show high accuracy, achieving a maximum binary F1‑score of 98% and multi‑class F1‑scores of 75% and 77%. The work aims to provide a standardized performance benchmark for medical image segmentation and a reference for other imaging scenarios.
arXiv Computer Vision
Oct 2
Multi-Resolution Feature Fusion U-Net for Magnetic Resonance Imaging Segmentation
arXiv:2610.00279v1 Announce Type: new Abstract: The segmentation of anatomical structures in medical images and particularly in MRI scans, is essential for clinical diagnosis and monitoring disease p...