Work

Accurate Segmentation of Pigmented Skin Lesions Using Grounded-segment-anything

Year: 2025

Type: article

Abstract: Takashi Nagaoka Melanoma is a highly aggressive form of skin cancer, and accurately segmenting pigmented lesions is crucial for early diagnosis. However, manual annotation is time-consuming, subjective, and requires expertise from dermatologists. To address these challenges, we propose a fully unsupervised segmentation approach using grounded-segment-anything model (Grounded-SAM), which operates without the need for annotated training data.

Source: Journal of Medical Imaging and Case Reports

DOI: 10.17756/micr.2025-116

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: diamond

Funder: Japan Society for the Promotion of Science

Award: Development of a Deep Learning-Based System for Automatic Melanoma Detection

OpenAlex ID: W4408919972

FWCI:

Cites:

Cited by:

Related to: 10