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
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
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