Predicting cell type counts in whole slide histology images using evidential multi-task learning

Predicting the presence and counting various cell types in the tumor micro-environment is crucial for identifying cell spatial and morphological features in computational pathology. Deep learning (DL) approaches have been used extensively in predicting and counting different cell types in whole slide images (WSIs). However, DL-based probabilistic approaches have limitations in classifying pixels and determining cellular boundaries. In this study, we propose an end-to-end evidential multi-task deep neural network for counting different cell types in hematoxylin and eosin-stained digital pathology images. The aim is to achieve a reasonable accuracy with lower uncertainties than state-of-the-art methods. Our proposed approach consists of one encoder and three task-specific decoders. The encoder utilizes a pre-trained ResNet50 model with ImageNet weights to extract contextual and spatial imaging features. The multi-tasking approach comprises of three tasks: predicting various cell type counts (main task), nuclei semantic segmentation and estimating cell-type density maps (auxiliary tasks). We evaluated the effectiveness of our proposed approach on two open-source datasets, PanNuke and MoNuSAC, which consist of 205,343 and 46,909 nuclei segmentation labels, respectively. Our results were compared with state-of-the-art methods, HoVer-Net, StarDist, and ALBRT. Our proposed approach showed superior performance in cell-type counting compared to HoVer-Net, ALBRT, and StarDist with relative improvements of 11%, 7%, and 5% on the PanNuke dataset test set, and 8.5%, 2%, and 11% on the MoNuSAC dataset test set, respectively, in terms of R-2. Our experiments suggest that our proposed approach can provide compelling interpretations of diverse cell types and is applicable for various downstream tasks in computational pathology.