mgr inż. Muhammad Usman | Gdańsk University of Technology

In the pursuit of advancing medical image segmentation, this study introduces a novel neural network, Efficient Deep Residual Network (EDRNet). Our approach is designed to handle different types of tumors, encompassing MRI brain tumor segmentation, three breast tumor ultrasound segmentation tasks, and cholorectal polyp segmentation from the Kvasir dataset. EDRNet incorporates advanced architectural features such as Enhanced Residual...

Visual perception plays a vital role in autonomous driving systems, demanding high accuracy and real-time inference speed to ensure safety. In this paper, we propose a multi-task framework that simultaneously performs object detection, drivable area segmentation, and lane line identification, addressing the requirements of accurate and efficient visual perception. Our approach utilizes a shared-encoder architecture with three separate...