Q-Segment: Segmenting Images In-Sensor for Vessel-Based Medical Diagnosis

arXiv:2312.09854v3 Announce Type: replace-cross
Abstract: This paper addresses the growing interest in deploying deep learning models directly in-sensor. We present "Q-Segment", a quantized real-time segmentation algorithm, and conduct a comprehensive evaluation on a low-power edge vision platform with an in-sensors processor, the Sony IMX500. One of the main goals of the model is to achieve end-to-end image segmentation for vessel-based medical diagnosis. Deployed on the IMX500 platform, Q-Segment achieves ultra-low inference time in-sensor only 0.23 ms and power consumption of …

abstract algorithm arxiv cs.cv deep learning diagnosis edge eess.iv evaluation images low medical paper platform power processor real-time segment segmentation sensor sensors sony type vision