Neural Cellular Automata for Weakly Supervised Segmentation of White Blood Cells

TL;DR

This paper introduces a novel weakly supervised segmentation method for white blood cells using neural cellular automata, enabling effective segmentation without extensive labeled data, and demonstrating superior performance on microscopy datasets.

Contribution

The paper presents a new approach combining neural cellular automata with weak supervision to perform segmentation without retraining on segmentation labels.

Findings

Outperforms existing weakly supervised methods on three datasets.

Enables segmentation using features from classification models.

Provides a scalable solution for medical image analysis.

Abstract

The detection and segmentation of white blood cells in blood smear images is a key step in medical diagnostics, supporting various downstream tasks such as automated blood cell counting, morphological analysis, cell classification, and disease diagnosis and monitoring. Training robust and accurate models requires large amounts of labeled data, which is both time-consuming and expensive to acquire. In this work, we propose a novel approach for weakly supervised segmentation using neural cellular automata (NCA-WSS). By leveraging the feature maps generated by NCA during classification, we can extract segmentation masks without the need for retraining with segmentation labels. We evaluate our method on three white blood cell microscopy datasets and demonstrate that NCA-WSS significantly outperforms existing weakly supervised approaches. Our work illustrates the potential of NCA for both classification and segmentation in a weakly supervised framework, providing a scalable and efficient solution for medical image analysis.