LeSAM: Adapt Segment Anything Model for Medical Lesion Segmentation.

The Segment Anything Model (SAM) is a foundational model that has demonstrated impressive results in the field of natural image segmentation. However, its performance remains suboptimal for medical image segmentation, particularly when delineating lesions with irregular shapes and low contrast. This can be attributed to the significant domain gap between medical images and natural images on which SAM was originally trained.

Unsharp Structure Guided Filtering for Self-Supervised Low-Dose CT Imaging.

Low-dose computed tomography (LDCT) imaging faces great challenges. Although supervised learning has revealed great potential, it requires sufficient and high-quality references for network training. Therefore, existing deep learning methods have been sparingly applied in clinical practice.

DeSpecNet: a CNN-based method for speckle reduction in retinal optical coherence tomography images.

Speckle is a major quality degrading factor in optical coherence tomography (OCT) images. In this work we propose a new deep learning network for speckle reduction in retinal OCT images, termed DeSpecNet. Unlike traditional algorithms, the model can learn from training data instead of manually selecting parameters such as noise level.