Deep Learning Segmentation and Classification of Red Blood Cells Using a Large Multi-Scanner Dataset

arXiv:2403.18468v1 Announce Type: cross
Abstract: Digital pathology has recently been revolutionized by advancements in artificial intelligence, deep learning, and high-performance computing. With its advanced tools, digital pathology can help improve and speed up the diagnostic process, reduce human errors, and streamline the reporting step. In this paper, we report a new large red blood cell (RBC) image dataset and propose a two-stage deep learning framework for RBC image segmentation and classification. The dataset is a highly diverse dataset of more …

abstract advanced artificial artificial intelligence arxiv cells classification computing cs.cv dataset deep learning diagnostic digital digital pathology eess.iv errors human intelligence pathology performance process reduce reporting segmentation speed tools type