A Robust Explainable AI (XAI) Technique for Tumour Classification

用于肿瘤分类的鲁棒可解释人工智能 (XAI) 技术

• 批准号: 2741280
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2741280
• 关键词: Robust; Explainable; AI; XAI; Technique

Recent developments in deep learning have sparked an interest in more high-stakes applications such as medical diagnostics. Given a medical scan, a clinician may want to differentiate between healthy and unhealthy tissue, or between pathologies. However, the black-box nature of DL models means their conclusions tend not to be trusted by clinicians who cannot determine how the model came to its decision. Explainable AI (XAI) techniques exist to bridge this gap by intuitively highlighting the most important features of an input. This gives the end-user an idea of how the model came to its conclusion. Knowing that a model's conclusion is correct is essential in medical diagnostics as their outcomes could impact lives.This project is based on expanding my UG4 Dissertation. During that project, I applied 3 XAI techniques to a medical imaging domain, in order to assess their reliability and usability in the diagnostic field, specifically breast tumour classification. I compared explanations generated by the techniques to each other, and to the opinions of 2 radiologists, and discovered that the techniques disagreed both with each other and with the medical truth. The techniques highlighted conflicting regions as most important to the classification decision. They also highlighted fragmented tumour regions, along with many irrelevant regions, and were deemed unhelpful for diagnostics by our clinicians. This work was also published in the 2022 iMIMIC workshop due to its relevance to the active XAI community.During this PhD I plan to further investigate the shortcomings of XAI techniques in the medical field, and generate a more robust piece of work which showcases a larger number of techniques and ML models, with a much more detailed clinician involvement. I then plan to use my findings to create an XAI technique which performs more reliably. Most techniques involve image segmentation - I plan to experiment with finding segmentation techniques that highlight clinical features, rather than just areas of an image with a similar appearance. I also plan to expand this research to other data modalities, for example gene sequences and tabular patient data.The overall goal of this project is to bring to light the problems with these XAI techniques, which are very successful in the computer vision domain but seemingly unreliable for medical diagnostics, and also to overcome these problems by creating a novel technique which takes into account the domain-specific knowledge I will have gathered during my analysis and communication with clinicians.

深度学习的最新发展引发了人们对医疗诊断等更高风险应用的兴趣。通过医学扫描，临床医生可能想要区分健康和不健康的组织，或病理。然而，深度学习模型的黑盒性质意味着它们的结论往往不被临床医生所信任，因为他们无法确定模型是如何做出决定的。可解释的人工智能 (XAI) 技术可以通过直观地突出显示输入的最重要特征来弥补这一差距。这让最终用户了解模型如何得出结论。了解模型的结论是否正确对于医学诊断至关重要，因为其结果可能会影响生活。该项目基于扩展我的 UG4 论文。在该项目期间，我将 3 种 XAI 技术应用于医学成像领域，以评估它们在诊断领域（特别是乳腺肿瘤分类）中的可靠性和可用性。我将这些技术产生的解释相互比较，并与 2 位放射科医生的意见进行比较，发现这些技术彼此不一致，而且与医学事实不一致。这些技术强调冲突区域对于分类决策来说是最重要的。它们还突出显示了碎片化的肿瘤区域以及许多不相关的区域，并且被我们的临床医生认为对诊断没有帮助。由于这项工作与活跃的 XAI 社区相关，因此也发表在 2022 年 iMIMIC 研讨会上。在攻读博士学位期间，我计划进一步研究 XAI 技术在医学领域的缺点，并生成更稳健的工作，展示更大的范围大量技术和机器学习模型，以及更详细的临床医生参与。然后，我计划利用我的发现来创建一种性能更可靠的 XAI 技术。大多数技术都涉及图像分割 - 我计划尝试寻找突出临床特征的分割技术，而不仅仅是具有相似外观的图像区域。我还计划将这项研究扩展到其他数据模式，例如基因序列和表格患者数据。该项目的总体目标是揭示这些 XAI 技术的问题，这些技术在计算机视觉领域非常成功，但看似医疗诊断不可靠，并且还通过创建一种新技术来克服这些问题，该技术考虑了我在分析和与临床医生沟通期间收集的特定领域知识。