avakanski/Attention-Enriched-DL-Model-for-Breast-Tumor-Segmentation
Salient attention U-Net model for tumor segmentation in breast ultrasound images, based on visual saliency maps
This tool helps radiologists and medical researchers accurately identify and outline breast tumors in ultrasound images. By taking an ultrasound image and a visual saliency map (showing where a radiologist's attention would typically focus), it produces a precise segmentation mask highlighting the tumor's boundaries. It is designed for medical professionals working with breast cancer diagnosis and research.
No commits in the last 6 months.
Use this if you need to precisely segment breast tumors from ultrasound images, especially when aiming for higher accuracy by incorporating expert visual attention insights.
Not ideal if you are working with other types of medical imaging or organs, or if you do not have access to visual saliency maps.
Stars
45
Forks
11
Language
Jupyter Notebook
License
—
Category
Last pushed
Jun 26, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/avakanski/Attention-Enriched-DL-Model-for-Breast-Tumor-Segmentation"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
dipy/dipy
DIPY is the paragon 3D/4D+ medical imaging library in Python. Contains generic methods for...
Project-MONAI/MONAI
AI Toolkit for Healthcare Imaging
Project-MONAI/MONAILabel
MONAI Label is an intelligent open source image labeling and learning tool.
neuronets/nobrainer
A framework for developing neural network models for 3D image processing.
axondeepseg/axondeepseg
Axon/Myelin segmentation using Deep Learning