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Project ID: P202208090001
Multi-Modality Fusion Coupled with Hybrid Machine Learning Systems for Improved Prediction of TNM Staging in Lung Cancer
Supervisor: Mohammadreza Salmanpour
Fund: Free
Status: on_going 8

Deep learning, Machine learning, Fusion Models


The project will be provided with datasets including PET, CT, their Ground truth. It will be provided with a strong GPU and google Colab to make long and heavy runs. The supervisor and advisors will lead and push all members to accomplish the project.

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Objective: Multi-level multi-modality-fusion-radiomics is a promising technique with the potential for improved prognostication of cancer. We aim to use advanced fusion techniques on PET and CT images coupled with Hybrid Machine Learning Systems to achieve improved TNM Stage prediction in Lung cancer (LC). Methods: In our study, 400 LC patients were included from The Cancer-Imaging-Archive (TCIA) and VGH in a multi-center-setting. We utilized hmls to train. each image underwent min-max-normalization, and followed by 5-fold-cross-validation. We employed 20 datasets including CT, PET, and 18 image-level-fused-datasets.

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