Authors: Atsushi Teramoto, Ayano Michiba, Yuka Kiriyama, Tetsuya Tsukamoto, Kazuyoshi Imaizumi, Hiroshi Fujita

Published on: March 26, 2024

Impact Score: 8.0

Arxiv code: Arxiv:2403.18151

Summary

• What is new: The study proposes a unique report-generation technique for lung cytology images that combines a CNN for image classification with a Transformer-based text decoder for generating reports, tailored to the classification results.
• Why this is important: Pulmonary cytology, involving cell morphology characterization for lung cancer diagnosis, is a highly labor-intensive and error-prone task.
• What the research proposes: A combined vision and text-generation model that uses CNN for classifying cytology images as benign or malignant, and then generates a detailed report using a Transformer-based text decoder.
• Results: Achieved 100% sensitivity and 96.4% specificity in classifying pulmonary cytology specimens, with generated reports showing high grammatical accuracy and better alignment with the gold standard compared to existing methods.

Technical Details

Technological frameworks used: Convolutional Neural Network (CNN) for image classification; Transformer for text generation.

Models used: CNN for classification, Transformer-based text decoders for benign and malignant reports.

Data used: 206 pulmonary cytology specimen images (71 benign, 135 malignant).

Potential Impact

Healthcare diagnostics, specifically lung cancer diagnosis companies, and digital pathology solution providers could see a significant automation and accuracy improvement.

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