cauchyturing/kaggle_diabetic_RAM
Extended Retinopathy Detection Challenge with the Regression Activation Map for visual explaination
This project helps ophthalmologists and medical researchers analyze retinal images to detect and categorize the severity of diabetic retinopathy (DR). It takes raw retinal scans as input and outputs a DR severity level, alongside a 'Regression Activation Map' (RAM) that visually highlights specific regions within the eye scan responsible for that diagnosis. This makes it easier for medical professionals to understand the model's reasoning and focus on critical areas.
No commits in the last 6 months.
Use this if you need an automated system to grade diabetic retinopathy from retinal images and want visual explanations for the diagnosis to aid clinical understanding.
Not ideal if you are looking for a certified medical device ready for direct clinical deployment without further validation and integration.
Stars
89
Forks
41
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Apr 24, 2017
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/cauchyturing/kaggle_diabetic_RAM"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
rsk97/Diabetic-Retinopathy-Detection
DIAGNOSIS OF DIABETIC RETINOPATHY FROM FUNDUS IMAGES USING SVM, KNN, and attention-based CNN...
JordiCorbilla/ocular-disease-intelligent-recognition-deep-learning
ODIR-2019: Ocular Disease Intelligent Recognition is a project leveraging state-of-the-art deep...
javathunderman/diabetic-retinopathy-screening
Diabetic retinopathy screening w/ Tensorflow.
koriavinash1/Optic-Disk-Cup-Segmentation
Optic Disc and Optic Cup Segmentation using 57 layered deep convolutional neural network
TheBeastCoding/glaucoma-dataset-metadata
Actively maintained and comprehensive public glaucoma dataset catalog