Find file History
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
..
Failed to load latest commit information.
Dev-Kit-IoT-Alarm
IDC-Classifier-GUI
IDC-Classifier/Inception
Results
images
IDC-Classifier-GUI.sln
README.md

README.md

Reducing False Negatives in the IDC Classifier

Intel® Movidius

Abstract

The Intel AI DevJam Demo project, Reducing False Negatives in the IDC Classifier, provides the source codes and tutorials for setting up the project that will be demonstrated at Intel AI DevJam at ICML (International Conference on Machine Learning) in Sweden, July 2018.

The Intel® AI DevJam Demo GUI uses a Windows application to communicate with a facial recognition classifier and an option of two classifiers trained to detect Invasive Ductal Carcinoma (Breast cancer) in histology images. The project combines the Invasive Ductal Carcinoma (IDC) Classification Using Computer Vision & IoT and TASS Movidius Facenet Classifier projects, along with some new improvements.

The goal of this project is to intentionally try to trick the model by using very similar, but opposite class, images from a small set of testing data that I believe humans may have difficulty telling apart. A larger set of testing data is provided to compare how the model works on larger datasets.

if we find false positives we will attempt to find a way to reduce them, providing a safety net for incorrect classifications that could mean the difference between life and death.

Intel® Movidius

Jump To Tutorials & Source Codes

About The Project

The Intel AI DevJam Demo uses a Windows application to communicate with a facial recognition classifier and a classifier trained to detect Invasive Ductal Carcinoma (Breast cancer) in histology images. The project combines the Invasive Ductal Carcinoma (IDC) Classification Using Computer Vision & IoT and TASS Movidius Facenet Classifier projects.

Invasive Ductal Carcinoma (IDC) Classification Using Computer Vision & IoT combines Computer Vision and the Internet of Things to provide a way to train a neural network with labelled breast cancer histology images to detect Invasive Ductal Carcinoma (IDC) in unseen/unlabelled images. There are two models available for the IDC classification, one using a custom trained Inception V3 model, and one using Cafe.

The TASS Movidius Facenet Classifier uses Siamese Neural Networks and Triplet Loss to classify known and unknown faces.

IoT Connectivity

IoT connectivity for the project is provided by the IoT JumpWay. The IoT JumpWay is an IoT communication platform as a service (PaaS) with a social network frontend. IoT JumpWay developers will soon be able to share projects/photos/videos and events. Use of the IoT JumpWay is completely free, you can find out more on the Developer Program page.

Universal Windows Application

Universal Windows Application

A Universal Windows Application allows training and querying the IDC and facial recognition classifiers, facial recognition training requires 1 image per person to be able to identify them. The Windows Universal Application also allows uploading histology images of IDC positive or negative slides for classification.

Testing The Universal Windows Application

Intel® Movidius & UP2

Intel® Movidius

For classification the project uses the Intel® Movidius and a custom trained Facenet to carry out facial classification, and a custom trained Inception V3 model for detecting Invasive Ductal Carcinoma (IDC) homed on an UP2.

Intel® UP2

The TASS Movidius Facenet Classifier uses Siamese Neural Networks and Triplet Loss to classify known and unknown faces.

Raspberry Pi 3b

Raspberry Pi 3b

An IoT connected alarm awaits messages for the results of the facial and IDC classification. In the event an unauthorized user is detected or IDC is detected an alarm (buzzer) is triggered and a red LED lights up, if both classifications are ok a blue LED lights up.

Bugs/Issues

Please feel free to create issues for bugs and general issues you come across whilst using this or any other IoT JumpWay Microsoft repo issues: IoT-JumpWay-Microsoft-Examples Github Issues

Contributors

Adam Milton-Barker, Intel® Software Innovator