Attention Networks and Optimized Deep Learning Architectures for Cancer Diagnosis and Prognosis in Medical Imaging

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The objective of this research is to design and develop novel deep learning architectures to address major challenged that Artificial Intelligence (AI) in Medicine faces. Although Convolutional Neural Networks (CNNs) have shown series of breakthroughs in Computer Vision and have achieved promising results in different Medical Imaging tasks such as cancer diagnosis (e.g., tumour detection) and prognosis, there are unmet challenges that impede their efficacy in translation into clinical settings. First, the visualization of CNNs' results is recognized as a major challenge for the integration of AI into clinical workflow and there is a knowledge gap in understanding how images contribute to the final results. Second, while the Area Under the receiver operating characteristic Curve (AUC) is the main evaluation metric for cancer diagnostic solutions in Medical Imaging, CNNs cannot be directly optimized for AUC, which may lead to suboptimal results. Third, as a preprocessing step, most CNN-based diagnostic and prognostic solutions rely on segmentation of tumour regions (region of interest or ROI). This is usually done manually by a clinician (e.g., radiologist) or automatically or semi-automatically by a segmentation algorithm, which is trained using the manual annotations. Because there is no definite way to confirm the exact boundaries of a tumour, the ROI annotation therefore heavily relies on radiologists' expertise and understanding of the underlying phenotype of the cancerous tissue and its appearance on the medical images. This leads to a wide variation of tumour regions annotated by different radiologists for the same case resulting in a significant variation in CNN models trained using ROIs. ROI variability significantly decreases the accuracy of AI-based diagnostic and prognostic models for a given label (e.g., patient survival). In this research, we will design, develop, and validate deep learning architectures that address these major challenges for AI in Medical Imaging. We will design and implement three different visualization methods for CNNs, which can be used for both CNN visualization and tumour localization at pixel level using image-level labels only. We will develop a genetic algorithm embedded into CNN architecture which enables the network to be directly optimized for AUC. Finally, we will develop a deep generative model that not only discovers the associations between medical images and the label (e.g., cancer grade), it also automatically generates the subregions in the image which drive such associations. We will apply the proposed solutions to different imaging modalities and cancer sites including brain tumours (MRI), prostate cancer (MRI), and lung cancer (CT). The outcome of this research will be novel solutions to harness meaningful information latent in imaging data to generate attention maps and significantly improve the performance and reliability of CNNs for both cancer diagnosis and prognosis in Medical Imaging.