  In digital pathology, both detection and classification of cells are
important for automatic diagnostic and prognostic tasks. Classifying cells into
subtypes, such as tumor cells, lymphocytes or stromal cells is particularly
challenging. Existing methods focus on morphological appearance of individual
cells, whereas in practice pathologists often infer cell classes through their
spatial context. In this paper, we propose a novel method for both detection
and classification that explicitly incorporates spatial contextual information.
We use the spatial statistical function to describe local density in both a
multi-class and a multi-scale manner. Through representation learning and deep
clustering techniques, we learn advanced cell representation with both
appearance and spatial context. On various benchmarks, our method achieves
better performance than state-of-the-arts, especially on the classification
task. We also create a new dataset for multi-class cell detection and
classification in breast cancer and we make both our code and data publicly
available.
