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学诊断等高风险应用的兴趣。考虑到医疗扫描，临床医生可能希望区分健康和不健康的组织，或者在病理学之间区分。但是，DL模型的黑盒性质意味着他们的结论往往不受临床医生的信任，他们无法确定模型如何做出决定。可以通过直观地强调输入的最重要特征来解释AI（XAI）技术来弥合这一差距。这为最终用户提供了一个关于模型如何得出结论的想法。知道模型的结论在医学诊断中是正确的，因为它们的结果可能会影响生活。该项目基于扩大我的UG4论文。在该项目期间，我将3种XAI技术应用于医学成像域，以评估其在诊断领域的可靠性和可用性，特别是乳腺肿瘤分类。我将这些技术产生的解释与2位放射科医生的观点进行了比较，并发现该技术彼此之间以及医学真相都不同意。这些技术强调了冲突的地区对分类决策最重要。他们还强调了碎片的肿瘤区域以及许多无关的地区，我们的临床医生认为诊断无益。这项工作还在2022年的IMIMIC研讨会上发表，因为它与活跃的XAI社区有关。在此博士学位我计划进一步研究医学领域XAI技术的缺点，并产生了更强大的工作，以展示更多的技术和ML模型，并具有更详细的临床参与。然后，我计划使用我的发现来创建XAI技术，该技术的性能更可靠。大多数技术都涉及图像分割 - 我计划尝试寻找临床特征的分割技术，而不仅仅是具有相似外观的图像的区域。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中非常成功，但对于医疗诊断而言似乎是不可靠的，并且可以通过创建这些新技术来解决这些问题，以解决域名的知识，我将在临床知识中进行访问，我将在我的分析中进行分析和分析。