WP-UNet: Weight Pruning U-Net with Depthwise Separable Convolutions for Semantic Segmentation of Kidney Tumors
Patike Kiran Rao, Subarna Chatterjee · Research Square · 2021
Abstract Background: The major challenge in medical imaging is to achieve high accuracy output during semantic image segmentation tasks in biomedical imaging while having fewer computational operations and faster inference. It is necessary in medical modalities such as kidney tumor CT scan activities, to assist radiologists. Several previous studies have carried out a complex deep network that requires high computational resources. However, a deep network on semantic segmentation of kidney tumor CT scans with fewer flops and parameters has not yet been evaluated.Methods: This research paper presents a novel network model called Weight Pruning U-Net (WP-UNet) which is extremely fast, compact, and computationally efficient to address this problem with kidney tumor CT scan images as an application. Results We apply the proposed deep network model on the kidney tumor CT scan image dataset on computational devices with limited resources for computing. We build a CNN model with minimum parameters inspired by the commonly adapted U-Net architecture of the deep convolution neural network model for CT scan image analysis by making use of a depthwise separable convolution functional layer in the entire network model. We proposed weight pruning with the depthwise separable and batch normalized UNet model to reach the expected performance and reduce the loss in the process. WP-UNet has 3 major benefits,- : (a) a lightweight model with a smaller size (b) fewer parameters, and (c) a faster assumption time with a less than floating point calculation with computational complexity (FLOPs). WP-UNet was tested on the KiTs challenge Biomedical CT Scan imaging Dataset for kidney tumor semantic segmentation (KiTs), and the results showed that comparable and often better results were obtained by the WP-UNet model compared to the existing state-of-the-art models.Conclusions: Unlike previous assumptions, our findings indicate that the architecture proposed is smaller than U-Net and demands 3x less computational complexity while retaining respectable accuracy results. Moreover it affects kidney tumor medical image analysis and their practical application.