Pathological Primitive Segmentation Based on Visual Foundation Model with Zero-Shot Mask Generation

arXiv:2404.08584v1 Announce Type: new
Abstract: Medical image processing usually requires a model trained with carefully crafted datasets due to unique image characteristics and domain-specific challenges, especially in pathology. Primitive detection and segmentation in digitized tissue samples are essential for objective and automated diagnosis and prognosis of cancer. SAM (Segment Anything Model) has recently been developed to segment general objects from natural images with high accuracy, but it requires human prompts to generate masks. In this work, we present a novel …

arxiv cs.cv foundation foundation model segmentation type visual zero-shot