Breast-Cancer_SAM_v1 Open-Source Breast Cancer Segmentation Model - Accurately Identify Tumor Areas in Medical Images

Breast Cancer SAM V1

Developed by ayoubkirouane

Breast cancer segmentation model based on Segment Anything Model (SAM), used for tumor region identification in medical imaging

Image Segmentation

Transformers

Supports Multiple LanguagesOpen Source License:Apache-2.0 #Breast Cancer Segmentation #Medical Image Analysis #Zero-shot Learning

Downloads 162

Release Time : 9/16/2023

Model Overview

This model is based on the SAM architecture, specifically designed for breast cancer medical image segmentation tasks, assisting clinicians in identifying and locating breast tumor regions to aid in diagnosis and treatment planning.

Model Features

Specialized for Medical Imaging

Optimized for breast cancer medical imaging, capable of accurately identifying tumor regions

Based on SAM Architecture

Utilizes the powerful Segment Anything Model foundation, offering excellent zero-shot performance

Interactive Segmentation

Supports generating high-quality object masks through input prompts (e.g., points or boxes)

Model Capabilities

Medical Image Segmentation

Tumor Region Identification

Interactive Segmentation

Zero-shot Transfer Learning

Use Cases

Medical Diagnosis

Breast Cancer Screening

Used for tumor detection in mammograms or ultrasound images

Aids in early detection of breast cancer lesions

Treatment Planning

Provides precise tumor location and extent information for surgery or radiotherapy

Improves treatment accuracy and reduces damage to healthy tissues

🚀 Breast Cancer Image Segmentation with SAM

This project focuses on breast cancer segmentation in medical images using the Segment Anything Model (SAM), which aids clinicians in disease diagnosis and treatment planning.

🚀 Quick Start

device = "cuda" if torch.cuda.is_available() else "cpu"
processor = SamProcessor.from_pretrained("wanglab/medsam-vit-base")
model = SamModel.from_pretrained("ayoubkirouane/Breast-Cancer_SAM_v1").to(device)

✨ Features

Medical Image Segmentation: Specialized in identifying and segmenting breast tumor regions in medical images like mammograms and ultrasound images.
State-of-the-Art Model: Utilizes the Segment Anything Model (SAM), a vision transformer-based model with excellent performance on various natural image segmentation tasks.

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

device = "cuda" if torch.cuda.is_available() else "cpu"
processor = SamProcessor.from_pretrained("wanglab/medsam-vit-base")
model = SamModel.from_pretrained("ayoubkirouane/Breast-Cancer_SAM_v1").to(device)

📚 Documentation

Description

Breast cancer segmentation is the task of identifying and segmenting the breast tumor region in medical images, such as mammograms and ultrasound images. This is an important task in the diagnosis and treatment of breast cancer, as it helps clinicians to better understand the extent of the disease and plan appropriate interventions.

The Segment Anything Model (SAM) is a state-of-the-art deep learning model for image segmentation. SAM is a vision transformer-based model that has been shown to achieve excellent performance on a variety of natural image segmentation tasks.

Base Model

The Segment Anything Model (SAM) produces high-quality object masks from input prompts such as points or boxes, and it can be used to generate masks for all objects in an image. It has been trained on a dataset of 11 million images and 1.1 billion masks and has strong zero-shot performance on a variety of segmentation tasks. https://github.com/facebookresearch/segment-anything

🔧 Technical Details

No technical details meeting the requirements are provided in the original document, so this section is skipped.

📄 License

The project is licensed under the Apache-2.0 license.

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