BayeSeg: Bayesian modeling for medical image segmentation with interpretable generalizability
Due to the cross-domain distribution shift aroused from diverse medical imaging systems,
many deep learning segmentation methods fail to perform well on unseen data, which limits …
many deep learning segmentation methods fail to perform well on unseen data, which limits …
Cross-scale fusion and domain adversarial network for generalizable rail surface defect segmentation on unseen datasets
Surface quality control is a crucial part of rail manufacturing. Deep neural networks have
shown impressive accuracy in rail surface defect segmentation under the assumption that …
shown impressive accuracy in rail surface defect segmentation under the assumption that …
MSAByNet: A multiscale subtraction attention network framework based on Bayesian loss for medical image segmentation
Medical image segmentation is a critical and complex process in medical image processing
and analysis. With the development of artificial intelligence, the application of deep learning …
and analysis. With the development of artificial intelligence, the application of deep learning …
Evidential Concept Embedding Models: Towards Reliable Concept Explanations for Skin Disease Diagnosis
Due to the high stakes in medical decision-making, there is a compelling demand for
interpretable deep learning methods in medical image analysis. Concept Bottleneck Models …
interpretable deep learning methods in medical image analysis. Concept Bottleneck Models …
KAER: Knowledge Adaptive Amalgamation of ExpeRts for Medical Images Segmentation
Recently, many foundation models for medical image analysis such as MedSAM,
SwinUNETR have been released and proven to be useful in multiple tasks. However …
SwinUNETR have been released and proven to be useful in multiple tasks. However …