Investigating the Use of Spatial Transformer Networks and Recurrent Neural Networks for Medical Image Segmentation

This paper investigates using popular deep learning models, Spatial Transformer Networks (STNs), and Recurrent Neural Networks (RNNs) for clinical picture segmentation. STNs are used to study a learnable pose-invariant body that permits image segmentation throughout exceptional segmentation tasks and affected person populace. RNNs seize temporal context within the facts and enhance segmentation accuracy. The paper evaluates the overall performance of the two models on the publicly available MIMIC-III dataset. It compares the outcomes with a segmentation model primarily based on the usual convolutional neural network (CNN) structure. The results reveal that the STN and RNN fashions gain superior overall performance compared to the usual CNN version in medical photograph segmentation. The look additionally highlights the capacity of deep studying fashions for addressing various demanding situations associated with clinical photograph segmentation.