Cell detection is a fundamental task in computational pathology that can be used for extracting high-level medical information from whole-slide images. For accurate cell detection, pathologists often zoom out to understand the tissue-level structures and zoom in to classify cells based on their morphology and the surrounding context. However, there is a lack of efforts to reflect such behaviors by pathologists in the cell detection models, mainly due to the lack of datasets containing both cell and tissue annotations with overlapping regions. To overcome this limitation, we propose and publicly release OCELOT, a dataset purposely dedicated to the study of cell-tissue relationships for cell detection in histopathology. OCELOT provides overlapping cell and tissue annotations on images acquired from multiple organs. Within this setting, we also propose multi-task learning approaches that benefit from learning both cell and tissue tasks simultaneously. When compared against a model trained only for the cell detection task, our proposed approaches improve cell detection performance on 3 datasets: proposed OCELOT, public TIGER, and internal CARP datasets. On the OCELOT test set in particular, we show up to 6.79 improvement in F1-score. We believe the contributions of this paper, including the release of the OCELOT dataset at https://lunit-io.github.io/research/publications/OCELOT are a crucial starting point toward the important research direction of incorporating cell-tissue relationships in computation pathology.

Objective assessment of atopic dermatitis (AD) is essential for choosing proper management strategies. This study investigated the performance of convolutional neural networks (CNN) models in grading the severity of AD. Five board-certified dermatologists independently evaluated the severity of 9,192 AD images. The severity of AD was evaluated based on an Investigator's Global Assessment (IGA) and six signs of AD. For CNN training, we applied three distinct approaches: 1) ensemble vs. integration 2) hard-label vs. soft-label and 3) train-set pruning. For the IGA prediction, the two best models were chosen based on the macro-averaged AUROC and F-1 score. The ensemble-soft-label-pruning model was chosen based on AUROC 0.943, 0.927 for the internal and external validation set respectively, and integration-soft-label-whole dataset model was chosen based on the F1-score 0.750, 0.721 for the internal and external validation set respectively. CNN models trained by multi-evaluator dataset outperformed the models by an individual evaluator dataset, and they performed better to the dataset in which the assessment of dermatologists was concordant. In conclusion, CNN models for AD could be improved by labeled dataset from multiple evaluators, merging methods with soft-label and train-set pruning.