Applications

AUCMEDI was already / is currently applied in multiple projects, challenges or clinical studies.
The Code for these applications is presented in separate GitHub repositories.

Even so these implementations are more complex than standard examples, they represent state-of-the-art and functional pipelines which were top-ranked in challenges or are successfully integrated in clinical environments.

Overview

Application	Type	AUCMEDI	Link
Multi-Disease Detection in Retinal Imaging	Challenge (rank #7)	v0.1.0	RIADD - Grand Challenge
Ensemble Learning Analysis	Research	v0.3.0	ENSMIC
Pneumonia Detector via Ensemble Learning	Research	v0.4.0	xray_pneumonia.ipynb
COVID-19 Severity Detection	Challenge (rank #5 - ongoing)	v0.4.0	COVID-19 Algorithm - Grand Challenge
Pathology - Gleason Score Estimation	Clinical Study	Latest	Ongoing

Multi-Disease Detection in Retinal Imaging

Preventable or undiagnosed visual impairment and blindness affects billion of people worldwide. Automated multi-disease detection models offer great potential to address this problem via clinical decision support in diagnosis. In this work, we proposed an innovative multi-disease detection pipeline for retinal imaging which utilizes ensemble learning to combine the predictive power of several heterogeneous deep convolutional neural network models. Our pipeline includes state-of-the-art strategies like transfer learning, class weighting, real-time image augmentation and focal loss utilization. Furthermore, we integrated ensemble learning techniques like heterogeneous deep learning models, bagging via 5-fold cross-validation and stacked logistic regression models.

Participation at the Retinal Image Analysis for multi-Disease Detection Challenge (RIADD): https://riadd.grand-challenge.org/

Reference:
Dominik Müller, Iñaki Soto-Rey and Frank Kramer. (2021)
Multi-Disease Detection in Retinal Imaging Based on Ensembling Heterogeneous Deep Learning Models
https://pubmed.ncbi.nlm.nih.gov/34545816/

Ensemble Learning Analysis

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 extend 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 (ensmic) 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 nine 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% F1-score increase. Augmenting showed consistent improvement capabilities by up to 4% and is also applicable to single model based pipelines. Cross-validation based Bagging demonstrated significant performance gain close to Stacking, which resulted in an F1-score increase up to +11%. Furthermore, we demonstrated that simple statistical pooling functions are equal or often even better than more complex pooling functions. We concluded that the integration of ensemble learning techniques is a powerful method for any medical image classification pipeline to improve robustness and boost performance.

Reference:
Dominik Müller, Iñaki Soto-Rey and Frank Kramer. (2022)
An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural Networks.
arXiv e-print: https://arxiv.org/abs/2201.11440

Pneumonia Detector via Ensemble Learning

In this work we use the AUCMEDI-Framework to train a deep neural network to classify chest X-ray images as either normal or viral pneumonia. Stratified k-fold cross-validation with k=3 is used to generate the validation-set and 15% of the data are set aside for the evaluation of the models of the different folds and ensembles each. A random-forest ensemble as well as a Soft-Majority-Vote ensemble are built from the predictions of the different folds. Evaluation metrics (Classification-Report, macro F1-scores, Confusion-Matrices, ROC-Curves) of the individual folds and the ensembles show that the classifier works well. Finally Grad-CAM and LIME explainable artificial intelligence (XAI) algorithms are applied to visualize the image features that are most important for the prediction. For Grad-CAM the heatmaps of the three folds are furthermore averaged for all images in order to calculate a mean XAI-heatmap. As the heatmaps of the different folds for most images differ only slightly this averaging procedure works well. However, only medical professionals can evaluate the quality of the features marked by the XAI. A comparison of the evaluation metrics with metrics of standard procedures such as PCR would also be important. Further limitations are discussed.

A manuscript describing this application was uploaded to arXiv:
https://arxiv.org/abs/2110.01017