Learning Where to Look: Differentiable Slice Selection and Efficient Channel Attention for FCD-II MRI Classification

Focal Cortical Dysplasia (FCD) is a major cause of drug-resistant epilepsy both in children and adults. In most such cases, surgery is the most effective treatment unless other treatments, such as rehabilitation, are the most effective intervention; hence, it is important to have the correct identification of FCD in order to make a clinical decision. Magnetic resonance imaging (MRI) is a common technique of imaging FCD because it is painless and offers good resolution images. Nevertheless, MRI is still a big challenge in detecting the lesions of FCD. The lesions are diverse in size and shape, and their location andimagingfeatures are usually subtle and atypical. Therefore, the process of manual identification is not only time consuming but also very much relies on the knowledge of the epileptologist, which brings inconsistency in diagnosis. To overcome these difficulties, this research is de voted to the automated detection of FCD lesions (i.e., FCD type-II) with the help of the state-of-the-art deep-learning methods. An automatic slice selection architecture based on Gumbel-softmax hard thresholding is proposed, which selects the top k important slices in a 3D MRI volume. The selected slices are then passed to Efficient Channel Attention (ECA) enhanced pre-trained Convolutional Neural Networks(CNNs) of DenseNet201, VGG16 and VGG19. The proposed method can detect the changes in healthy brain tissue, FCD-II lesions and T1w features by comparing FCD II lesions and T1w features with healthy brain tissue us ing FCD-II, T1-weighted (T1w) and FLAIR MRI sequences. Amongthese models, ECA-DenseNet201 demonstrated the best performance in classification, achieving high accuracy (96.7% for FLAIR and 96.8% for T1w), precision (0.972 for FLAIR and 0.957 for T1W), and F1-score (0.953 for FLAIR and 0.967 for T1W) in distinguishing FCD-II slices from healthy brain slices.