A full pipeline to analyze lung histopathology images

Lluis Borràs Ferrís; Simon Püttmann; Niccoló Marini; Simona Vatrano; Filippo Fragetta; Alessandro Caputo; Francesco Ciompi; Manfredo Atzori; Henning Müller

doi:10.1117/12.3006708

3 April 2024 A full pipeline to analyze lung histopathology images

Lluis Borràs Ferrís, Simon Püttmann, Niccoló Marini, Simona Vatrano, Filippo Fragetta, Alessandro Caputo, Francesco Ciompi, Manfredo Atzori, Henning Müller

Author Affiliations +

Proceedings Volume 12933, Medical Imaging 2024: Digital and Computational Pathology; 1293303 (2024) https://doi.org/10.1117/12.3006708
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Histopathology images involve the analysis of tissue samples to diagnose several diseases, such as cancer. The analysis of tissue samples is a time-consuming procedure, manually made by medical experts, namely pathologists. Computational pathology aims to develop automatic methods to analyze Whole Slide Images (WSI), which are digitized histopathology images, showing accurate performance in terms of image analysis. Although the amount of available WSIs is increasing, the capacity of medical experts to manually analyze samples is not expanding proportionally. This paper presents a full automatic pipeline to classify lung cancer WSIs, considering four classes: Small Cell Lung Cancer (SCLC), non-small cell lung cancer divided into LUng ADenocarcinoma (LUAD) and LUng Squamous cell Carcinoma (LUSC), and normal tissue. The pipeline includes a self-supervised algorithm for pre-training the model and Multiple Instance Learning (MIL) for WSI classification. The model is trained with 2,226 WSIs and it obtains an AUC of 0.8558 ± 0.0051 and a weighted f1-score of 0.6537 ± 0.0237 for the 4-class classification on the test set. The capability of the model to generalize was evaluated by testing it on the public The Cancer Genome Atlas (TCGA) dataset on LUAD and LUSC classification. In this task, the model obtained an AUC of 0.9433 ± 0.0198 and a weighted f1-score of 0.7726 ± 0.0438.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Lluis Borràs Ferrís, Simon Püttmann, Niccoló Marini, Simona Vatrano, Filippo Fragetta, Alessandro Caputo, Francesco Ciompi, Manfredo Atzori, and Henning Müller "A full pipeline to analyze lung histopathology images", Proc. SPIE 12933, Medical Imaging 2024: Digital and Computational Pathology, 1293303 (3 April 2024); https://doi.org/10.1117/12.3006708

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