  Confocal Laser Endomicroscope (CLE) is a novel handheld fluorescence imaging
device that has shown promise for rapid intraoperative diagnosis of brain tumor
tissue. Currently CLE is capable of image display only and lacks an automatic
system to aid the surgeon in analyzing the images. The goal of this project was
to develop a computer-aided diagnostic approach for CLE imaging of human glioma
with feature localization function. Despite the tremendous progress in object
detection and image segmentation methods in recent years, most of such methods
require large annotated datasets for training. However, manual annotation of
thousands of histopathological images by physicians is costly and time
consuming. To overcome this problem, we propose a Weakly-Supervised Learning
(WSL)-based model for feature localization that trains on image-level
annotations, and then localizes incidences of a class-of-interest in the test
image. We developed a novel convolutional neural network for diagnostic
features localization from CLE images by employing a novel multiscale
activation map that is laterally inhibited and collaterally integrated. To
validate our method, we compared proposed model's output to the manual
annotation performed by four neurosurgeons on test images. Proposed model
achieved 88% mean accuracy and 86% mean intersection over union on intermediate
features and 87% mean accuracy and 88% mean intersection over union on
restrictive fine features, while outperforming other state of the art methods
tested. This system can improve accuracy and efficiency in characterization of
CLE images of glioma tissue during surgery, augment intraoperative
decision-making process regarding tumor margin and affect resection rates.
