Medical image segmentation is a fundamental and critical step in many image-guided
clinical approaches. Recent success of deep learning-based segmentation methods usually …

Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …

We present SegGPT, a generalist model for segmenting everything in context. We unify
various segmentation tasks into a generalist in-context learning framework that …

Implicit neural representations (INRs) have emerged as a powerful paradigm in scene
reconstruction and computer graphics, showcasing remarkable results. By utilizing neural …

Medical image analysis plays an important role in clinical diagnosis. In this paper, we
examine the recent Segment Anything Model (SAM) on medical images, and report both …

With the advances of data-driven machine learning research, a wide variety of prediction
problems have been tackled. It has become critical to explore how machine learning and …

Automatic segmentation of retinal vessels in fundus images plays an important role in the
diagnosis of some diseases such as diabetes and hypertension. In this paper, we propose …

Deep learning (DL) based semantic segmentation methods have been providing state-of-the-
art performance in the last few years. More specifically, these techniques have been …

Open-vocabulary semantic segmentation presents the challenge of labeling each pixel
within an image based on a wide range of text descriptions. In this work we introduce a …

Both researchers and malware authors have demonstrated that malware scanners are
unfortunately limited and are easily evaded by simple obfuscation techniques. This paper …