Cancer Detec - Lung Cancer Diagnosis Support System

Principal Investigator

Name
Nelson Faria

Degrees
Bachelor

Institution
Polytechnic Institute of Cávado and Ave

Position Title
Student

Email
a14805@alunos.ipca.pt

About this CDAS Project

Study
NLST (Learn more about this study)

Project ID
NLST-818

Initial CDAS Request Approval
Jul 22, 2021

Title
Cancer Detec - Lung Cancer Diagnosis Support System

Summary
Lung cancer is the type of cancer that causes most deaths worldwide and, the sooner it is discovered, more possibilities there are for the patient to be treated. An accurate histological classification of tumours is essential for lung cancer diagnosis and adequate patient management. Whole-slide images (WSI) generated from tissue samples can be analysed using Deep Learning technology to assist pathologists. In the literature review, it is given an overview of the lung cancer, exploring the different types of implementations done until the present. These methods show a two-step implementation in which the tasks consist primarily on the detection of the tumour and after on the histologic classification of the tumour. To detect the neoplastic cells, the WSI is split in patches, and then a convolutional neural network is applied to identify and generate a heatmap highlighting the tumour regions. In the next step, the features are extracted from the cancerous regions and submitted in a classifier to determine the histologic type of tumour present in each patch. In this project, should be developed a solution based on the literature review to surpass the limitations found in the actual models, and with better performance and accuracy, that could be used as an aid in the pathological diagnosis of lung cancer.

Aims

This consists in a Master Degree Thesis project and the aim is to develop an automatic system, based on medical images, that will support the diagnosis of lung cancer.

Collaborators

Nelson Faria
Sofia Campelos
Vitor Carvalho
2Ai, School of Technology, Polytechnic Institute of Cávado and Ave
IPATIMUP - Institute of Pathology and Molecular Immunology