Detecting Metastatic Lung Cancer and Its Lesions From CT-Scan Images Using Deep Interpretable Networks

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Rasekh, Ali | 2021

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 54663 (19)
University: Sharif University of Technology
Department: Computer Engineering
Advisor(s): Rabiee, Hamid Reza
Abstract:
Using automated assistants in medical applications has been increased in recent years. One of the most popular methods are artificial intelligence and deep learning methods which are specifically used in medical images analysis. Using these methods can improve the diagnosis accuracy, while performing in a faster time. So these methods can reduce the economical costs, error rate, and response time. But one important challenge in deep learning methods, is the interpretability of neural networks. In this research we focused on introducing an interpretability method for our pixel-wise segmentation network which is applied to the lung nodules dataset. In this research we first implemented a segmentation method to segment metastatic lung nodules. Then checked different definitions of interpretability of segmentation networks by dividing the task into three sub-problems. The results provide useful information about both positive and negative effects of different pixels and lung areas on nodule segmentation.
Keywords:
Metastasis ; Cancer ; Deep Learning ; Neural Network ; Interpretability ; CT Scan ; Medical Images ; Lung Cancer