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

• URL: http://arxiv.org/abs/2107.10912v1
• Date: Thu, 22 Jul 2021 20:16:34 GMT
• Title: Explainable artificial intelligence (XAI) in deep learning-based medical image analysis
• Authors: Bas H.M. van der Velden, Hugo J. Kuijf, Kenneth G.A. Gilhuijs, Max A. Viergever
• Abstract summary: 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 categorized according to the framework and according to anatomical location. The paper concludes with an outlook of future opportunities for XAI in medical image analysis.
• Score: 3.255042271092803
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 categorized according to the framework and according to anatomical location. The paper concludes with an outlook of future opportunities for XAI in medical image analysis.

Related papers

• Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks [9.93411316886105]
  Self-eXplainable AI (S-XAI) incorporates explainability directly into the training process of deep learning models. This survey presents a comprehensive review across various image modalities and clinical applications.
  arXiv  Detail & Related papers  (2024-10-03T09:29:28Z)
• Survey on Adversarial Attack and Defense for Medical Image Analysis: Methods and Challenges [64.63744409431001]
  We present a comprehensive survey on advances in adversarial attacks and defenses for medical image analysis. For a fair comparison, we establish a new benchmark for adversarially robust medical diagnosis models.
  arXiv  Detail & Related papers  (2023-03-24T16:38:58Z)
• Computer Vision on X-ray Data in Industrial Production and Security Applications: A survey [89.45221564651145]
  This survey reviews the recent research on using computer vision and machine learning for X-ray analysis in industrial production and security applications. It covers the applications, techniques, evaluation metrics, datasets, and performance comparison of those techniques on publicly available datasets.
  arXiv  Detail & Related papers  (2022-11-10T13:37:36Z)
• A Review of Causality for Learning Algorithms in Medical Image Analysis [12.249809900292798]
  We analyze machine learning for medical image analysis within the framework of Technology Readiness Levels. We review how causal analysis methods can fill a gap when creating robust and adaptable medical image analysis algorithms.
  arXiv  Detail & Related papers  (2022-06-11T11:04:13Z)
• Explainable Deep Learning Methods in Medical Image Classification: A Survey [0.0]
  State-of-the-art deep learning models have achieved human-level accuracy on the classification of different types of medical data. These models are hardly adopted in clinical, mainly due to their lack of interpretability. The black-box-ness of deep learning models has raised the need for devising strategies to explain the decision process of these models.
  arXiv  Detail & Related papers  (2022-05-10T09:28:14Z)
• Artificial Intelligence in Tumor Subregion Analysis Based on Medical Imaging: A Review [2.119165920735065]
  This paper reviews AI-based tumor subregion analysis in medical imaging. We categorize the AI-based methods by training strategy: supervised and unsupervised. Specific challenges and potential AI applications in tumor subregion analysis are discussed.
  arXiv  Detail & Related papers  (2021-03-25T03:41:21Z)
• Variational Knowledge Distillation for Disease Classification in Chest X-Rays [102.04931207504173]
  We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays. We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
  arXiv  Detail & Related papers  (2021-03-19T14:13:56Z)
• Machine Learning Methods for Histopathological Image Analysis: A Review [62.14548392474976]
  Histopathological images (HIs) are the gold standard for evaluating some types of tumors for cancer diagnosis. One of the ways of accelerating such an analysis is to use computer-aided diagnosis (CAD) systems.
  arXiv  Detail & Related papers  (2021-02-07T19:12:32Z)
• Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization [112.2628296775395]
  Clinical decision support using deep neural networks has become a topic of steadily growing interest. clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend. We propose a novel decision explanation scheme based on CycleGAN activation which generates high-quality visualizations of classifier decisions even in smaller data sets.
  arXiv  Detail & Related papers  (2020-10-09T14:39:27Z)
• Learning Binary Semantic Embedding for Histology Image Classification and Retrieval [56.34863511025423]
  We propose a novel method for Learning Binary Semantic Embedding (LBSE) Based on the efficient and effective embedding, classification and retrieval are performed to provide interpretable computer-assisted diagnosis for histology images. Experiments conducted on three benchmark datasets validate the superiority of LBSE under various scenarios.
  arXiv  Detail & Related papers  (2020-10-07T08:36:44Z)
• Survey of XAI in digital pathology [3.4591414173342643]
  We present a survey on XAI within digital pathology, a medical imaging sub-discipline with particular characteristics and needs. We give a thorough overview of current XAI techniques of potential relevance for deep learning methods in pathology imaging. In doing, we incorporate uncertainty estimation methods as an integral part of the XAI landscape.
  arXiv  Detail & Related papers  (2020-08-14T13:11:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.