.png){kind=icon}
返回
Explainability and causability in digital pathology
.png){kind=icon}
OA
AI
DOI：10.1002/cjp2.322.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The current move towards digital pathology enables pathologists to use artificial intelligence (AI)-based computer programmes for the advanced analysis of whole slide images. However, currently, the best-performing AI algorithms for image analysis are deemed black boxes since it remains - even to their developers - often unclear why the algorithm delivered a particular result. Especially in medicine, a better understanding of algorithmic decisions is essential to avoid mistakes and adverse effects on patients. This review article aims to provide medical experts with insights on the issue of explainability in digital pathology. A short introduction to the relevant underlying core concepts of machine learning shall nurture the reader's understanding of why explainability is a specific issue in this field. Addressing this issue of explainability, the rapidly evolving research field of explainable AI (XAI) has developed many techniques and methods to make black-box machine-learning systems more transparent. These XAI methods are a first step towards making black-box AI systems understandable by humans. However, we argue that an explanation interface must complement these explainable models to make their results useful to human stakeholders and achieve a high level of causability, i.e. a high level of causal understanding by the user. This is especially relevant in the medical field since explainability and causability play a crucial role also for compliance with regulatory requirements. We conclude by promoting the need for novel user interfaces for AI applications in pathology, which enable contextual understanding and allow the medical expert to ask interactive 'what-if'-questions. In pathology, such user interfaces will not only be important to achieve a high level of causability. They will also be crucial for keeping the human-in-the-loop and bringing medical experts' experience and conceptual knowledge to AI processes.
Keyword:
digital pathology
artificial intelligence
explainability
causability
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.7
论文数:
341
被引数:
1.1K
机构
引用论文
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications解释深度神经网络及其以后: 方法和应用综述.png)
PROCEEDINGS OF THE IEEEOAAI
PROCEEDINGS OF THE IEEEIF25.9
OAAI分享收藏Artificial intelligence to identify genetic alterations in conventional histopathology人工智能识别常规组织病理学中的遗传改变JOURNAL OF PATHOLOGYOAAI
JOURNAL OF PATHOLOGYIF5.2
OAAI分享收藏Hidden Variables in Deep Learning Digital Pathology and Their Potential to Cause Batch Effects: Prediction Model Study深度学习数字病理学中的隐藏变量及其导致批量效应的潜力: 预测模型研究
OAAI
OAAI分享收藏A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence可解释人工智能的对比和反事实解释生成方法综述IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge前列腺癌诊断和Gleason分级的人工智能: 熊猫挑战NATURE MEDICINEOAAI
NATURE MEDICINEIF50
OAAI分享收藏Comparative Analysis Reveals Distinct and Overlapping Functions of Mef2c and Mef2d during Cardiogenesis in Xenopus laevisPLoS ONEOAAI
PLoS ONEIF0
OAAI分享收藏
查看更多内容