File size: 1,438 Bytes
b2d31d9 9e736f6 b2d31d9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 |
---
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: image-segmentation
tags:
- medical
- segmentation
datasets:
- nielsr/breast-cancer
---
## 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**
## Get Started with the Model
``` python
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)
``` |