Multitask learning approach for lung nodule segmentation and classification in CT images

• Type: Article in International Conference Proceedings Book
• Date: 2023

Authors

Fernandes, L (Author) Other; Oliveira, HP (Author) FCUP

Conference proceedings International

Title: Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

Pages: 3874-3880

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

Istanbul, 5 December 2023 through 8 December 2023

Indexing

Scopus - 4 Citations

Other information

Authenticus ID: P-00Z-WKE

DOI: 10.1109/bibm58861.2023.10385868

Abstract (EN): Amongst the different types of cancer, lung cancer is the one with the highest mortality rate and consequently, there is an urgent need to develop early detection methods to improve the survival probabilities of the patients. Due to the millions of deaths that are caused annually by cancer, there is large interest int the scientific community to developed deep learning models that can be employed in computer aided diagnostic tools.Currently, in the literature, there are several works in the Radiomics field that try to develop new solutions by employing learning models for lung nodule classification. However, in these types of application, it is usually required to extract the lung nodule from the input images, while using a segmentation mask made by a radiologist. This means that in a clinical scenario, to be able to employ the developed learning models, it is required first to manually segment the lung nodule. Considering the fact that several patients are attended daily in the hospital with suspicion of lung cancer, the segmentation of each lung nodule would become a tiresome task. Furthermore, the available algorithms for automatic lung nodule segmentation are not efficient enough to be used in a real application.In response to the current limitations of the state of the art, the proposed work attempts to evaluate a multitasking approach where both the segmentation and the classification task are executed in parallel. As a baseline, we also study a sequential approach where first we employ DL models to segment the lung nodule, corp the lung nodule from the input image and then finally, we classify the cropped nodule. Our results show that the multitasking approach is better than to sequentially execute the segmentation and classification task for lung nodule classification. For instances, while the multitasking approach was able to achieve an AUC of 84.49% in the classification task, the sequential approach was only able to achieve an AUC of 72.43%. These results show that the proposed multitasking approach can become a viable alternative to the classification and segmentation of lung nodules.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6