YOLOX-based framework for nuclei detection on whole-slide histopathological RGB and hyperspectral images

Abstract

The current advances in Whole-Slide Imaging (WSI) scanners allow for more and better visualization of histological slides. However, the analysis of histological samples by visual inspection is subjective and could be challenging. State-of-the-art object detection algorithms can be trained for cell spotting in a WSI. In this work, a new framework for the detection of tumor cells in high-resolution and high-detail using both RGB and Hyperspectral (HS) imaging is proposed. The framework introduces techniques to be trained on partially labeled data, since labeling at the cellular level is a time and energy-consuming task. Furthermore, the framework has been developed for working with RGB and HS information reduced to 3 bands. Current results are promising, showcasing in RGB similar performance as reference works (F1-score = 66.2%) and high possibilities for the integration of reduced HS information into current state-of-art deep learning models, with current results improving the mean precision a 6.3% from synthetic RGB images.