An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural Networks

01/27/2022
by   Dominik Müller, et al.
122

Novel and high-performance medical image classification pipelines are heavily utilizing ensemble learning strategies. The idea of ensemble learning is to assemble diverse models or multiple predictions and, thus, boost prediction performance. However, it is still an open question to what extent as well as which ensemble learning strategies are beneficial in deep learning based medical image classification pipelines. In this work, we proposed a reproducible medical image classification pipeline for analyzing the performance impact of the following ensemble learning techniques: Augmenting, Stacking, and Bagging. The pipeline consists of state-of-the-art preprocessing and image augmentation methods as well as 9 deep convolution neural network architectures. It was applied on four popular medical imaging datasets with varying complexity. Furthermore, 12 pooling functions for combining multiple predictions were analyzed, ranging from simple statistical functions like unweighted averaging up to more complex learning-based functions like support vector machines. Our results revealed that Stacking achieved the largest performance gain of up to 13 improvement capabilities by up to 4 based pipelines. Cross-validation based Bagging demonstrated to be the most complex ensemble learning method, which resulted in an F1-score decrease in all analyzed datasets (up to -10 statistical pooling functions are equal or often even better than more complex pooling functions. We concluded that the integration of Stacking and Augmentation ensemble learning techniques is a powerful method for any medical image classification pipeline to improve robustness and boost performance.

READ FULL TEXT

page 3

page 6

page 7

page 8

page 9

page 11

10/21/2019

MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning

The increased availability and usage of modern medical imaging induced a...
06/09/2021

Rethink Transfer Learning in Medical Image Classification

Transfer learning (TL) with deep convolutional neural networks (DCNNs) h...
05/31/2022

Deep learning pipeline for image classification on mobile phones

This article proposes and documents a machine-learning framework and tut...
03/26/2021

Multi-Disease Detection in Retinal Imaging based on Ensembling Heterogeneous Deep Learning Models

Preventable or undiagnosed visual impairment and blindness affect billio...
06/23/2022

A novel adversarial learning strategy for medical image classification

Deep learning (DL) techniques have been extensively utilized for medical...
11/01/2021

Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities

In this extended abstract, we will present and discuss opportunities and...
07/17/2020

A Technical Report for VIPriors Image Classification Challenge

Image classification has always been a hot and challenging task. This pa...