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Quantitative Fundus Autofluorescence for the Evaluation of Retinal Diseases

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Published on: March 11, 2016

Multiple serous cavity effusion screening based on smear images using vision transformer.

Chunbao Wang^1,2, Xiangyu Wang², Zeyu Gao³

¹Department of Pathology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China.

Scientific Reports

|March 29, 2024

Summary

A new vision transformer (ViT) model accurately detects malignant cells in serous cavity effusions. This deep learning approach significantly improves diagnostic accuracy and efficiency for cytologists.

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Area of Science:

Pathology
Medical Imaging
Artificial Intelligence

Background:

Serous cavity effusion diagnosis relies on cytological smear examination, a convenient yet limited method.
Traditional methods face challenges in efficiency and accuracy for detecting malignant cells.

Purpose of the Study:

To develop an improved method for precise malignant cell detection in serous cavity effusions.
To introduce a transformer-based classification framework utilizing the vision transformer (ViT) model.

Main Methods:

Collected smear images and cytological reports from 161 patients.
Annotated 4836 image patches to create a dataset for smear image classification.
Developed and validated a vision transformer (ViT) model and compared it with a convolutional neural network (CNN).

Main Results:

The ViT model achieved an AUROC of 0.99 for patch classification, outperforming the CNN (AUROC 0.86).
External validation showed the ViT model maintained high performance with an AUROC of 0.98 at the patient level (CNN: 0.84).
Model visualization confirmed precise identification of malignant cell regions.

Conclusions:

Deep learning, particularly the ViT model, can automate screening for serous cavity effusions.
The ViT model enhances diagnostic accuracy and efficiency for cytologists.
ViT's self-attention mechanism is ideal for analyzing small, sparse cellular targets in effusions.