Efficient Transfer Learning using Pre-trained Models on CT/MRI

Thesis (Master's)--University of Washington, 2023The medical imaging field has unique obstacles to face when performing computer vision classification tasks. The retrieval of the data, be it CT scans or MRI, is not only expensive but also limited due to the lack of publicly available labeled data. In spite of this, clinicians often need this medical imaging data to perform diagnosis and recommendations for treatment. This motivates the use of efficient transfer learning techniques to not only condense the complexity of the data as it is often volumetric, but also to achieve better results faster through the use of established machine learning techniques like transfer learning, fine-tuning, and shallow deep learning. In this paper, we introduce a three-step process to perform classification using CT scans and MRI data. The process makes use of fine-tuning to align the pretrained model with the target class, feature extraction to preserve learned information for downstream classification tasks, and shallow deep learning to perform subsequent training. Experiments are done to compare the performance of the proposed methodology as well as the time cost trade offs for using our technique compared to other baseline methods. Through these experiments we find that our proposed method outperforms all other baselines while achieving a substantial speed up in overall training time