  The work presented explores the use of denoising autoencoders (DAE) for brain
lesion detection, segmentation and false positive reduction. Stacked denoising
autoencoders (SDAE) were pre-trained using a large number of unlabeled patient
volumes and fine tuned with patches drawn from a limited number of patients
(n=20, 40, 65). The results show negligible loss in performance even when SDAE
was fine tuned using 20 patients. Low grade glioma (LGG) segmentation was
achieved using a transfer learning approach wherein a network pre-trained with
High Grade Glioma (HGG) data was fine tuned using LGG image patches. The weakly
supervised SDAE (for HGG) and transfer learning based LGG network were also
shown to generalize well and provide good segmentation on unseen BraTS 2013 &
BraTS 2015 test data. An unique contribution includes a single layer DAE,
referred to as novelty detector(ND). ND was trained to accurately reconstruct
non-lesion patches using a mean squared error loss function. The reconstruction
error maps of test data were used to identify regions containing lesions. The
error maps were shown to assign unique error distributions to various
constituents of the glioma, enabling localization. The ND learns the non-lesion
brain accurately as it was also shown to provide good segmentation performance
on ischemic brain lesions in images from a different database.
