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 Human-AI Collaborative Multi-modal Multi-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.