Abstract
Endometriosis, affecting about 10\% of individuals assigned female at birth, is challenging to diagnose and manage. Diagnosis typically involves the identification of various signs of the disease using either laparoscopic surgery or the analysis of T1/T2 MRI images, with the latter being quicker and cheaper but less accurate. A key diagnostic sign of endometriosis is the obliteration of the Pouch of Douglas (POD). However, even experienced clinicians struggle with accurately classifying POD obliteration from MRI images, which complicates the training of reliable AI models. In this paper, we introduce the \underline{H}uman-\underline{AI} \underline{Co}llaborative \underline{M}ulti-modal \underline{M}ulti-rater Learning (HAICOMM) methodology to address the challenge above. HAICOMM is the first method that explores three important aspects of this problem: 1) multi-rater learning to extract a cleaner label from the multiple ``noisy'' labels available per training sample; 2) multi-modal learning to leverage the presence of T1/T2 MRI images for training and testing; and 3) human-AI collaboration to build a system that leverages the predictions from clinicians and the AI model to provide more accurate classification than standalone clinicians and AI models. Presenting results on the multi-rater T1/T2 MRI endometriosis dataset that we collected to validate our methodology, the proposed HAICOMM model outperforms an ensemble of clinicians, noisy-label learning models, and multi-rater learning methods.
Abstract (translated)
子宫内膜异位症，大约10%的出生时被分配为女性的个体，诊断和治疗具有挑战性。诊断通常涉及通过腹腔镜手术或分析T1/T2 MRI图像来识别各种病情的症状，后者的速度更快、费用更低，但准确性较低。子宫内膜异位症的关键诊断指标是输卵管囊肿（POD）的闭锁。然而，经验丰富的临床医生也难以准确地对MRI图像中的POD闭锁进行分类，这使得可靠的人工智能模型的训练变得复杂。在本文中，我们提出了《H》uman-AI Cooperative Multi-modal Multi-rater Learning (HAICOMM) 方法来应对上述挑战。HAICOMM是第一个探索这个问题三个重要方面（1）多评分学习从每个训练样本中提取更清晰的标签；2）多模态学习利用T1/T2 MRI图像的 presence for training 和 testing；3）人机合作构建一个利用医生和AI模型的预测提供更准确分类的系统。我们在验证我们方法的multi-rater T1/T2 MRI endometriosis数据集上展示结果，与临床医生、噪声标签学习和多评分学习方法组成的集成模型相比，HAICOMM模型表现优异。
URL
https://arxiv.org/abs/2409.02046
