Medulloblastoma Tumor Classification using Deep Transfer Learning with Multi-Scale EfficientNets

TL;DR

This study demonstrates that transfer learning with multi-scale EfficientNets significantly improves automated classification of medulloblastoma subtypes from histopathological images, aiding pathologists with a robust, high-performance tool.

Contribution

The paper systematically evaluates EfficientNets for medulloblastoma classification, showing that larger input resolutions and transfer learning enhance accuracy over traditional CNNs.

Findings

Pre-trained EfficientNets outperform other CNN architectures.

Larger input resolutions improve classification performance.

Transfer learning is crucial for large CNN architectures.

Abstract

Medulloblastoma (MB) is the most common malignant brain tumor in childhood. The diagnosis is generally based on the microscopic evaluation of histopathological tissue slides. However, visual-only assessment of histopathological patterns is a tedious and time-consuming task and is also affected by observer variability. Hence, automated MB tumor classification could assist pathologists by promoting consistency and robust quantification. Recently, convolutional neural networks (CNNs) have been proposed for this task, while transfer learning has shown promising results. In this work, we propose an end-to-end MB tumor classification and explore transfer learning with various input sizes and matching network dimensions. We focus on differentiating between the histological subtypes classic and desmoplastic/nodular. For this purpose, we systematically evaluate recently proposed EfficientNets, which uniformly scale all dimensions of a CNN. Using a data set with 161 cases, we demonstrate that pre-trained EfficientNets with larger input resolutions lead to significant performance improvements compared to commonly used pre-trained CNN architectures. Also, we highlight the importance of transfer learning, when using such large architectures. Overall, our best performing method achieves an F1-Score of 80.1%.