  An important task at the onset of a laparoscopic cholecystectomy (LC)
operation is the inspection of gallbladder (GB) to evaluate the thickness of
its wall, presence of inflammation and extent of fat. Difficulty in
visualization of the GB wall vessels may be due to the previous factors,
potentially as a result of chronic inflammation or other diseases. In this
paper we propose a multiple-instance learning (MIL) technique for assessment of
the GB wall vascularity via computer-vision analysis of images from LC
operations. The bags correspond to a labeled (low vs. high) vascularity dataset
of 181 GB images, from 53 operations. The instances correspond to unlabeled
patches extracted from these images. Each patch is represented by a vector with
color, texture and statistical features. We compare various state-of-the-art
MIL and single-instance learning approaches, as well as a proposed MIL
technique based on variational Bayesian inference. The methods were compared
for two experimental tasks: image-based and video-based (i.e. patient-based)
classification. The proposed approach presents the best performance with
accuracy 92.1% and 90.3% for the first and second task, respectively. A
significant advantage of the proposed technique is that it does not require the
time-consuming task of manual labelling the instances.
