From fee8341c80ab94d237eb70fea48229dfe64b6157 Mon Sep 17 00:00:00 2001 From: Aleksei Tiulpin Date: Sun, 22 Oct 2017 20:08:12 +0300 Subject: [PATCH] Update README.md --- README.md | 21 ++++++++++++++++++++- 1 file changed, 20 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 8d80506..138e047 100644 --- a/README.md +++ b/README.md @@ -7,8 +7,27 @@ Codes for paper **Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs Osteoarthritis (OA) is the 11th highest disability factor and it is associated with the cartilage and bone degeneration in the joints. The most common type of is knee OA and it is causing an extremly high economical burden to the society while being difficult to diagnose. In this study we present a novel Deep Learning-based approach to diagnose knee osteoarthritis from plain radiographs (X-ray images). -## Benchmarks +## Benchmarks and how-to-run Here we present the training codes and the pretrained models from each of our experiments. Please, see the paper for more details. +To run the experiments, you should have the following main demendencies: + +* pytorch +* PIL +* matplotlib +* Jupyter Notebook (to work with attention maps) +* tqdm +* visdom +* numpy +* termcolor +* torchvision + +run corresponding bash files to obtain the results (validation is visualized in visdom). + +## Attention maps examples + +## License + +This code is freely available only for research purpuses.