Multi-Class Cell Detection Using Spatial Context Representation

TL;DR

This paper introduces a novel deep learning approach that leverages spatial context and statistical functions to improve multi-class cell detection and classification in digital pathology, outperforming existing methods.

Contribution

It presents a new method combining spatial statistical functions with deep clustering for enhanced cell detection and classification, along with a new dataset for breast cancer analysis.

Findings

Outperforms state-of-the-art methods on benchmarks

Effective in multi-class cell classification tasks

Provides publicly available code and dataset

Abstract

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.