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The use of convolutional neural networks (CNNs) for classification tasks has become dominant in various medical imaging applications. At the same time, recent advances in interpretable machine learning techniques have shown great potential in explaining classifiers decisions. Layer-wise relevance propagation (LRP) has been introduced as one of these novel methods that aim to provide visual interpretation for the networks decisions. In this work we propose the application of 3D CNNs with LRP for the first time for neonatal T2-weighted magnetic resonance imaging (MRI) data analysis. Through LRP, the decisions of our trained classifier are transformed into heatmaps indicating each voxels relevance for the outcome of the decision. Our resulting LRP heatmaps reveal anatomically plausible features in distinguishing preterm neonates from term ones.
Deep convolutional networks often append additive constant (bias) terms to their convolution operations, enabling a richer repertoire of functional mappings. Biases are also used to facilitate training, by subtracting mean response over batches of tr
The model parameters of convolutional neural networks (CNNs) are determined by backpropagation (BP). In this work, we propose an interpretable feedforward (FF) design without any BP as a reference. The FF design adopts a data-centric approach. It der
Quantitative ultrasound (QUS) can reveal crucial information on tissue properties such as scatterer density. If the scatterer density per resolution cell is above or below 10, the tissue is considered as fully developed speckle (FDS) or low-density s
We propose two deep neural network architectures for classification of arbitrary-length electrocardiogram (ECG) recordings and evaluate them on the atrial fibrillation (AF) classification data set provided by the PhysioNet/CinC Challenge 2017. The fi
We introduce a convolutional recurrent neural network (CRNN) for music tagging. CRNNs take advantage of convolutional neural networks (CNNs) for local feature extraction and recurrent neural networks for temporal summarisation of the extracted featur