arXiv:2311.14875
Bayesian Neural Networks for 2D MRI Segmentation
원문 초록
Uncertainty quantification is vital for safety-critical Deep Learning applications like medical image segmentation. We introduce BA U-Net, an uncertainty-aware model for MRI segmentation that integrates Bayesian Neural Networks with Attention Mechanisms. BA U-Net delivers accurate, interpretable results, crucial for reliable pathology screening. Evaluated on BraTS 2020, this model addresses the critical need for confidence estimation in deep learning-based medical imaging.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Autoadaptive Medical Segment Anything Model
Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster
TextBraTS: Text-Guided Volumetric Brain Tumor Segmentation with Innovative Dataset Development and Fusion Module Exploration
Simple is what you need for efficient and accurate medical image segmentation
SWDL: Stratum-Wise Difference Learning with Deep Laplacian Pyramid for Semi-Supervised 3D Intracranial Hemorrhage Segmentation
Unified Medical Image Segmentation with State Space Modeling Snake
토론
이 논문과 연결된 포럼 글이 아직 없습니다.