ninatu / anomaly_detection Goto Github PK
View Code? Open in Web Editor NEWThis is the official implementation of "Anomaly Detection with Deep Perceptual Autoencoders".
License: Apache License 2.0
This is the official implementation of "Anomaly Detection with Deep Perceptual Autoencoders".
License: Apache License 2.0
I am having a hard time downloading the CAMELYON16 dataset. Can anyone please provide a source from where you could download the dataset? Thanks.
Hello.
Thank you very much for sharing your admirable study to the public!
I would like to train and test my pathological images to detect anomalies by using your dpa model.
Would you mind letting me know where should I put my train, validation, test data and several anomaly images to choose hyperparameter, and how config file should be changed, to use your dpa?
Thank you,
I encountered this error while running deep_if model. Can you please add this module?
Hi,
Thanks you once again for your wonderful work and help that you did last time (almost a year ago) in downloading the Camelyon16 dataset. After a long break, I am once again trying to play a bit with your work. I downloaded and have preprocessed the Camelyon16 dataset. Now my goal is to run your perceptual autoencoder to find the anomalies.
However, I am not sure how I can do that and therefore, I need your guidance on this.
The main issue is I guess I need to know which config file I should use. I couldn't find any config for Camelyon16 in "anomaly_detection/configs/dpa" folder. However, there are several in "anomaly_detection/configs/dpa/camelyon16/final". Could you tell me what "class_healthy" folder and all the subfolders, i.e., run_0, run_1, run_2 actually means?
What I want to do is to run a bunch of test images and get the result that shows which images are anomalies among these.
Your help on this would be highly appreciated.
Thanks in advance.
Hi,
Here may be a bug ,
In Line 28 of camelyon16_preprocessing/scripts/1_convert_annotation_to_json.py,
X = list(map(lambda x: float(x.get('x')) should be changed to
X = list(map(lambda x: float(x.get('X'))
Best wishes
Hi,
Thanks for your great work! I have a question about the early stopping. According to your paper, 200 tumor patches (abnormal samples) were used as the validation set. In your code, I noticed that the sum of the adversarial loss and reconstruction loss of the validation set was used as the sign for early stopping. I am a little confused why the loss score of abnormal samples in the validation set instead of the AUC score of validation sets with both abnormal and normal images available is used for early stopping.
I am looking forward to your reply. Thank you!
A declarative, efficient, and flexible JavaScript library for building user interfaces.
๐ Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. ๐๐๐
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google โค๏ธ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.