Vision Transformers for Weakly-Supervised Microorganism Enumeration

TL;DR

This paper compares vision transformers and traditional models for counting microorganisms in images, showing ViTs perform competitively and opening new research avenues in microbial image analysis.

Contribution

It provides a comparative analysis of ViTs versus ResNets for microorganism counting, highlighting ViTs' potential in weakly-supervised microbial enumeration.

Findings

ResNets outperform ViTs overall

ViTs show competent results across datasets

ViTs open new research directions in microbial counting

Abstract

Microorganism enumeration is an essential task in many applications, such as assessing contamination levels or ensuring health standards when evaluating surface cleanliness. However, it's traditionally performed by human-supervised methods that often require manual counting, making it tedious and time-consuming. Previous research suggests automating this task using computer vision and machine learning methods, primarily through instance segmentation or density estimation techniques. This study conducts a comparative analysis of vision transformers (ViTs) for weakly-supervised counting in microorganism enumeration, contrasting them with traditional architectures such as ResNet and investigating ViT-based models such as TransCrowd. We trained different versions of ViTs as the architectural backbone for feature extraction using four microbiology datasets to determine potential new approaches for total microorganism enumeration in images. Results indicate that while ResNets perform better overall, ViTs performance demonstrates competent results across all datasets, opening up promising lines of research in microorganism enumeration. This comparative study contributes to the field of microbial image analysis by presenting innovative approaches to the recurring challenge of microorganism enumeration and by highlighting the capabilities of ViTs in the task of regression counting.