Corpus ID: 236318184

Explainable artificial intelligence (XAI) in deep learning-based medical image analysis

  title={Explainable artificial intelligence (XAI) in deep learning-based medical image analysis},
  author={Bas H. M. van der Velden and Hugo J. Kuijf and Kenneth G. A. Gilhuijs and Max A. Viergever},
With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis. This survey presents an overview of eXplainable Artificial Intelligence (XAI) used in deep learning-based medical image analysis. A framework of XAI criteria is introduced to classify deep learning-based medical image analysis methods. Papers on XAI techniques in medical image analysis are then surveyed and… Expand

Figures and Tables from this paper


HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach
A hierarchical classification of Celiac Disease Severity is performed using the Hierarchical Medical Image classification (HMIC) approach, which uses stacks of deep learning models to give particular comprehension at each level of the clinical picture hierarchy. Expand
Gradient-based Interpretation on Convolutional Neural Network for Classification of Pathological Images
  • Jun-zhong Ji
  • Computer Science
  • 2019 International Conference on Information Technology and Computer Application (ITCA)
  • 2019
This work trained a CNN model on digital pathological images from lymph nodes sections to classify metastatic tissue, achieving F1 score of over 86% and investigates the possible ways to understand how the model makes decisions using gradient-based method. Expand
Versatile Framework for Medical Image Processing and Analysis with Application to Automatic Bone Age Assessment
Experimental results indicate that the proposed framework can be effectively applied to medical image analysis task and apply to the bone age assessment (BAA) task using RSNA dataset and achieve the state-of-the-art performance. Expand
A survey on deep learning in medical image analysis
This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year, to survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. Expand
Deep Learning for Medical Image Analysis
Different novel methods based on deep learning for brain abnormality detection, recognition, and segmentation for analyzing medical images using deep learning algorithm are explored. Expand
Explainable skin lesion diagnosis using taxonomies
This work proposes to leverage medical knowledge, in particular the taxonomic organization of skin lesions, which will be used to develop a hierarchical neural network and recent advances in channel and spatial attention modules, which can identify interpretable features and regions in dermoscopy images. Expand
Explainable AI for medical imaging: deep-learning CNN ensemble for classification of estrogen receptor status from breast MRI
A novel transfer-trained dual-domain DCNN architecture derived from the AlexNet model trained on ImageNet data that received the spatial and dynamic components of each DCE-MRI ROI as input and was evaluated with the area under the receiver operating characteristic curve (AUC) from leave-one-case-out crossvalidation. Expand
Explaining Machine Learning-Based Classifications of In-Vivo Gastral Images
This paper proposes an explainable machine learning tool that can potentially be used for decision support in medical image analysis scenarios and presents initial experiments that have been performed on in-vivo gastral images obtained from capsule endoscopy. Expand
Visual Interpretation of Convolutional Neural Network Predictions in Classifying Medical Image Modalities
The proposed “Class-selective Relevance Mapping” (CRM) method is significantly better in detecting and localizing the discriminative ROIs than other state of the art class-activation methods. Expand
Multi-task learning for mortality prediction in LDCT images
A multi-task learning framework is introduced, which can simultaneously extract image features from LDCT images and estimate the clinical measurements for all-cause mortality risk prediction and shows that the extracted feature vectors have improved mortality prediction. Expand