Efficient Cancer Classification by Coupling Semi Supervised and Multiple Instance Learning

Arne Schmidt

doi:10.1109/ACCESS.2022.3143345

Efficient Cancer Classification by Coupling Semi Supervised and Multiple Instance Learning

2022-01-01

15

OA

AI

A

Arne Schmidt *

J

Julio Silva-Rodríguez

DOI：10.1109/ACCESS.2022.3143345

Original

Original request for help

Save

Abstract

En 中文

The annotation of large datasets is often the bottleneck in the successful application of artificial intelligence in computational pathology. For this reason recently Multiple Instance Learning (MIL) and Semi Supervised Learning (SSL) approaches are gaining popularity because they require fewer annotations. 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. Our method is able to learn from the global WSI diagnosis and a combination of labeled and unlabeled patches. Furthermore, we propose and evaluate an efficient labeling paradigm that guarantees a strong classification performance when combined with our learning framework. We compare our method to SSL and MIL baselines, the state-of-the-art and completely supervised training. With only a small percentage of patch labels our proposed model achieves a competitive performance on SICAPv2 (Cohen's kappa of 0.801 with 450 patch labels), PANDA (Cohen's kappa of 0.794 with 22,023 patch labels) and Camelyon16 (ROC AUC of 0.913 with 433 patch labels). Our code is publicly available at https://github.com/arneschmidt/ssl_and_mil_cancer_classification.

Keywords:

Annotations

Training

Cancer

Labeling

Solid modeling

Deep learning

Technological innovation

Cancer classification

histopathology

multiple instance learning

semi-supervised learning

whole slide images

Efficient Cancer Classification by Coupling Semi Supervised and Multiple Instance Learning

Abstract

Journal

Organization