A Robust Explainable AI (XAI) Technique for Tumour Classification

深度学习的最新发展激发了人们对更高风险的应用程序的兴趣，比如医疗诊断。对于医学扫描，临床医生可能想要区分健康和不健康的组织，或者病理。然而，DL模型的黑箱性质意味着他们的结论往往不受临床医生的信任，他们无法确定模型是如何做出决定的。可解释人工智能(XAI)技术通过直观地突出输入的最重要功能来弥合这一差距。这让最终用户了解该模型是如何得出结论的。在医学诊断学中，了解模型的结论是正确的是至关重要的，因为它们的结果可能会影响生活。这个项目是基于我的UG4学位论文展开的。在该项目中，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相互比较，并与两位放射科医生的意见进行比较，发现这些技术彼此不一致，也与医学事实不符。这些技术突出了相互冲突的地区对分类决策最重要。它们还突出了零散的肿瘤区域，以及许多不相关的区域，临床医生认为这些区域对诊断没有帮助。这项工作也发表在2022年iMIMIC研讨会上，因为它与活跃的XAI社区相关。在此期间，我计划进一步调查XAI技术在医学领域的缺点，并生成一篇更健壮的作品，展示更多的技术和ML模型，以及更详细的临床医生参与。然后，我计划使用我的发现来创建一种执行更可靠的XAI技术。大多数技术涉及图像分割--我计划试验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，但在医疗诊断方面似乎不可靠，并通过创建一种新的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时收集的特定领域的知识。

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.