Brain Attention Regularized Network
Hydrocephalus is a medical condition characterized by an abnormal accumulation of cerebrospinal fluid (CSF) in the brain. Identification of postinfectious hydrocephalus (PIH) verses non-postinfectious hydrocephalus (NPIH), as well as the pathogen involved in PIH is crucial for developing an appropriate treatment plan. Unlike conventional classification tasks, this problem is particularly challenging as there is a great deal of overlap between the visual patterns that guide the classification of hydrocephalus images into PIH and NPIH on computed tomography (CT) scans. Moreover, the size and shape of the head vary significantly across different hydrocephalic patients, making it even more difficult to identify consistent features for classification. These challenges are only exacerbated for the task of identifying the pathogen within a PIH scan. State-of-the-art classification performance is achieved via deep convolutional neural networks (CNNs). However, deep learning often relies on generous training data and may produce class activations that are not physically meaningful. To address the aforementioned challenges, first, we introduce a novel brain attention regularizer on a 2D DenseNet that forces the CNN to put more focus inside brain regions that are crucial for classification. Often, information from only 2D slices may not be sufficient to obtain reasonable performance. Therefore, to capture additional inter-slice information, we add a 3D CNN branch to the existing 2D CNN branch. Then, a mutual attention regularization loss term is introduced to the training of the network which enables the two CNN branches to share information and puts more attention on important regions that are distributed between slices in a given CT stack. To incorporate this regularizer effectively, an alternative optimization strategy is employed to handle the stability issues that are common in training a 3D CNN. Since we introduce attention regularizers to brain image classification, we call our 2D CNN the brain attention regularized network (BAR-net) and we refer to the hybrid 2D/3D CNN as the mutual brain attention regularized network (MBAR-net).
|Work Title||Brain Attention Regularized Network|
|License||CC BY-NC-ND 4.0 (Attribution-NonCommercial-NoDerivatives)|
|Work Type||Software Or Program Code|
|Publication Date||August 26, 2021|
|Deposited||August 25, 2021|
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