Multi-Class Cell Detection Using Spatial Context Representation
This addresses the problem of accurate cell classification for pathologists in diagnostic tasks, though it appears incremental by building on existing methods with added spatial features.
The paper tackles the challenge of detecting and classifying cell subtypes in digital pathology by incorporating spatial context, achieving better performance than state-of-the-art methods, especially in classification tasks.
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.