BaT-SAM: Introducing Inductive Bias into SAM via a Mixture of Convolutional Experts for Brain Tumor Segmentation
cs.CV
Submitted: 2026-10-11
Updated: 2026-10-11
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
- SAM-Med2D
- XCT-SAM: Sequential Parameter-Efficient Domain Adaptation of SAM for Industrial XCT Defect Segmentation
- LoRA: Low-Rank Adaptation of Large Language Models
- Segmenting Medical Images: From UNet to Res-UNet and nnUNet
- Pyramid Attention Network for Semantic Segmentation
- Towards Complex Backgrounds: A Unified Difference-Aware Decoder for Binary Segmentation
- Attention U-Net: Learning Where to Look for the Pancreas
- Boundary-Aware Segmentation Network for Mobile and Web Applications
- Efficient and Accurate Pneumonia Detection Using a Novel Multi-Scale Transformer Approach
- Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model
Related papers
- Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
- AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
- Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
- MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
- TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
- A Survey on Efficient Vision-Language-Action Models