Adaptive Neural Layer for Globally Filtered Segmentation.

This study is motivated by typical images taken during ultrasonic examinations in the clinic. Their grainy appearance, low resolution, and poor contrast demand an eye of a very qualified expert to discern targets and to spot pathologies. Training a segmentation model on such data is frequently accompanied by excessive pre-processing and image adjustments, with an accumulation of the localization error emerging due to the digital post-filtering artifacts and due to the annotation uncertainty. Each patient case generally requires an individually tuned frequency filter to obtain optimal image contrast and to optimize the segmentation quality. Thus, we aspired to invent an adaptive global frequency-filtering neural layer to "learn" optimal frequency filter for each image together with the weights of the segmentation network itself. Specifically, our model receives the source image in the spatial domain, automatically selects the necessary frequencies from the frequency domain, and transmits the inverse-transform image to the convolutional neural network for concurrent segmentation. In our experiments, such "learnable" filters boosted typical U-Net segmentation performance by 10% and made the training of other popular models (DenseNet and ResNet) almost twice faster. In our experiments, this trait holds both for two public datasets with ultrasonic images (breast and nerves), and for natural images (Caltech birds).