Class Activation Map-based Weakly supervised Hemorrhage Segmentation
  using Resnet-LSTM in Non-Contrast Computed Tomography images
Shreyas H Ramananda Vaanathi Sundaresan
In clinical settings, intracranial hemorrhages (ICH) are routinely diagnosed
using non-contrast CT (NCCT) for severity assessment. Accurate automated
segmentation of ICH lesions is the initial and essential step, immensely useful
for such assessment. However, compared to other structural imaging modalities
such as MRI, in NCCT images ICH appears with very low contrast and poor SNR.
Over recent years, deep learning (DL)-based methods have shown great potential,
however, training them requires a huge amount of manually annotated
lesion-level labels, with sufficient diversity to capture the characteristics
of ICH. In this work, we propose a novel weakly supervised DL method for ICH
segmentation on NCCT scans, using image-level binary classification labels,
which are less time-consuming and labor-efficient when compared to the manual
labeling of individual ICH lesions. Our method initially determines the
approximate location of ICH using class activation maps from a classification
network, which is trained to learn dependencies across contiguous slices. We
further refine the ICH segmentation using pseudo-ICH masks obtained in an
unsupervised manner. The method is flexible and uses a computationally light
architecture during testing. On evaluating our method on the validation data of
the MICCAI 2022 INSTANCE challenge, our method achieves a Dice value of 0.55,
comparable with those of existing weakly supervised method (Dice value of
0.47), despite training on a much smaller training data.