Project MyDeepLearn

End-to-end web application for image analysis using neural networks

The web application MyDeepLearn
© Lena Wiese
Our tool for CNN-based image analysis can detect the shape of a skin lesion in images and classify the lesion. Screenshot of the web application, showing the prediction and classification of the uploaded images.

Analyzing large sets of image data takes a lot of effort and is still a manual process that requires expert knowledge, especially with biomedical images. Convolutional neural networks (CNNs) have an enormous potential in the medical field due to their ability to extract important features automatically. To make this technology available to a broad spectrum of end users, Fraunhofer researchers have developed the intuitive, web-based end-to-end pipeline MyDeepLearn: It supports users almost from the beginning with a partly automated, easy-to-access pipeline with high usability that reduces the amount of time and manual work. The end-to-end web application enables, for example, detection of skin alterations in melanoma images and classification of skin cancer using neural networks. 

Valuable support for early diagnosis 

MyDeepLearn links the CNNs in the back end with the web application in the front end. It provides users with a vector-based editing tool to post-process predictions or generate annotations, in addition to visualizing the data and providing different evaluation methods. It helps researchers and physicians to evaluate large sets of image data and supports early diagnosis – in particular in situations where there are only few annotated data available, as is often the case in the medical domain.

“Human-in-the-loop” approach

The web application can improve the CNNs iteratively with an interactive "human-in-the-loop" approach, which means that the user is involved in the evaluation and can actively change it. The workflow thus provides users with constantly improving technology, while continually expanding the data set to make this technology even more accurate.

Functionality of the web application

© Fraunhofer ITEM

Our services

Providing access to a web application for segmentation and evaluation of biomedical image data

Providing access to a powerful infrastructure

Your benefits

  • Complete end-to-end pipeline with easy access via a web browser
  • No installations required
  • Software always up to date due to cloud access
  • No hardware infrastructure and maintenance required
  • High throughput of image data due to the powerful Fraunhofer cloud
  • High usability

Training of the neural network for individual use cases

Implementation of individual evaluation methods

Your contact at Fraunhofer ITEM

Lena Wiese

Contact Press / Media

Prof. Dr. Lena Wiese

Manager of the Working Group on Bioinformatics / Head of Attract Group Bioinformatics “IDA – intelligent data analysis for good health and chemical safety“

Phone +49 511 5350-303