ESR8 – Efficient Cancer Classification by Coupling Semi Supervised and Multiple Instance Learning

In this work we couple SSL and MIL to train a deep learning classifier that combines the advantages of both methods and overcomes their limitations.

Hello there! I’m happy to announce that his first article as a first author was recently published in IEEE Access, a multidisciplinary journal which is Q1 in computer science and engineering.

For me, computational pathology was a completely new field which yields some very specific challenges for machine learning algorithms – Whole Slide Images have very high resolutions and labeled data is often scarce. In this article we provide a possible solution for these challenges by coupling multiple instance and semi supervised learning. This allows us to train a machine learning model with a small percentage of labeled data while still maintaining a high performance. To know more, please check out the article https://ieeexplore.ieee.org/document/9681818.

The perspective to actually help people with better diagnostic tools is a huge motivation and I am curious about everything yet to come. 

Proposed training framework, combining semi supervised and multiple instance learning for efficient cancer classification. It utilizes the global WSI diagnosis, unlabeled patches and a limited amount of labeled patches for training. The model requires less labels while showing a performance comparable to supervised state-of-the-art methods.

Thanks for the great collaboration to Julio Silva Rodríguez, Valery Naranjo Ornedo and my supervisor Rafael Molina 

Arne Schmidt – ESR8