  Endometriosis is a common chronic gynecological disorder that has many
characteristics, including the pouch of Douglas (POD) obliteration, which can
be diagnosed using Transvaginal gynecological ultrasound (TVUS) scans and
magnetic resonance imaging (MRI). TVUS and MRI are complementary non-invasive
endometriosis diagnosis imaging techniques, but patients are usually not
scanned using both modalities and, it is generally more challenging to detect
POD obliteration from MRI than TVUS. To mitigate this classification imbalance,
we propose in this paper a knowledge distillation training algorithm to improve
the POD obliteration detection from MRI by leveraging the detection results
from unpaired TVUS data. More specifically, our algorithm pre-trains a teacher
model to detect POD obliteration from TVUS data, and it also pre-trains a
student model with 3D masked auto-encoder using a large amount of unlabelled
pelvic 3D MRI volumes. Next, we distill the knowledge from the teacher TVUS POD
obliteration detector to train the student MRI model by minimizing a regression
loss that approximates the output of the student to the teacher using unpaired
TVUS and MRI data. Experimental results on our endometriosis dataset containing
TVUS and MRI data demonstrate the effectiveness of our method to improve the
POD detection accuracy from MRI.
