Learning to count small and clustered objects with application to bacterial colonies

TL;DR

This paper introduces ACFamNet Pro, an advanced neural network model that improves bacterial colony counting accuracy by handling small, clustered objects and enhancing cross-species generalisation.

Contribution

The paper presents ACFamNet Pro, a novel extension of FamNet with attention and residuals, specifically designed for small, clustered bacterial colonies and better cross-species performance.

Findings

ACFamNet Pro achieves a mean normalized absolute error of 9.64%.

It outperforms FamNet and ACFamNet by 12.71% and 2.23%, respectively.

The model effectively handles small, clustered objects with improved generalisation.

Abstract

Automated bacterial colony counting from images is an important technique to obtain data required for the development of vaccines and antibiotics. However, bacterial colonies present unique machine vision challenges that affect counting, including (1) small physical size, (2) object clustering, (3) high data annotation cost, and (4) limited cross-species generalisation. While FamNet is an established object counting technique effective for clustered objects and costly data annotation, its effectiveness for small colony sizes and cross-species generalisation remains unknown. To address the first three challenges, we propose ACFamNet, an extension of FamNet that handles small and clustered objects using a novel region of interest pooling with alignment and optimised feature engineering. To address all four challenges above, we introduce ACFamNet Pro, which augments ACFamNet with multi-head attention and residual connections, enabling dynamic weighting of objects and improved gradient flow. Experiments show that ACFamNet Pro achieves a mean normalised absolute error (MNAE) of 9.64% under 5-fold cross-validation, outperforming ACFamNet and FamNet by 2.23% and 12.71%, respectively.