On April 13^th, 2022 at 1.00 pm, a new appointment of NECSTSpecialTalk titled "On How to Push Efficient Medical Semantic Segmentation to the Edge: the SENECA approach" will be held online via Facebook by Raffaele Berzoini, Biomedical Engineering Master Student at Politecnico di Milano.

Semantic segmentation is the process of assigning each input image pixel a value representing a class, and it enables the clustering of pixels into object instances. It is a highly employed computer vision task in various fields such as autonomous driving and medical image analysis. In particular, in medical practice, semantic segmentation identifies different regions of interest within an image, like different organs or anomalies such as tumors. Fully Convolutional Networks (FCNs) have been employed to solve semantic segmentation in different fields and found their way in the medical one. In this context, the low contrast among semantically different areas, the constraint related to energy consumption, and computation resource avail[1]ability increase the complexity and limit their adoption in daily practice. Based on these considerations, we propose SENECA to bring medical semantic segmentation to the edge with high energy efficiency and low segmentation time while preserving the accuracy. We reached a throughput of 335.4 ± 0.34 frames per second on the FPGA, 4.65× better than its GPU counterpart, with a global dice score of 93.04% ± 0.07 and an improvement in terms of energy efficiency with respect to the GPU of 12.7×.

The NECSTLab is a DEIB laboratory, with different research lines on advanced topics in computing systems: from architectural characteristics, to hardware-software codesign methodologies, to security and dependability issues of complex system architectures.

Streaming via Facebook will be available at the following link