Medical imaging is one of the cornerstones of clinical diagnosis, providing insights into the anatomy and physiology of organs and tissues for screening, initial diagnosis, treatment planning, and follow-up. Utilizing both invasive and non-invasive techniques, medical imaging employs various contrast mechanisms to capture details of the tissue structure and the functionality of biological systems at different spatial and temporal resolutions, and dimensionalities. The ever-growing volume of medical image data driven by screening programs, digitalization, and the push towards precision medicine has highlighted the need for automatic image analysis methods to reduce the workload of healthcare personnel in reviewing these images.

Deep learning (DL), a subset of artificial intelligence (AI), comprises of methods that learn representations from data to perform various predictive tasks. Although DL was introduced in the mid-1960s, it has only been successfully applied for computer vision tasks in the past two decades, becoming the standard method for natural image processing. Additionally, the versatility of DL in processing data from diverse sources (such as speech, text, and climate) has encouraged its application in the medical domain as well.

This thesis explores the application of DL-based methods for medical image analysis, focusing on cancer diagnosis at various treatment planning stages, including preoperative, intraoperative, and postoperative procedures. Methods were developed and applied to three medical imaging modalities: optical coherence tomography (OCT) for intraoperative diagnosis, magnetic resonance imaging (MRI) for pre-operative diagnosis and radiotherapy treatment planning, and histopathology whole-slide images (WSI) for postoperative final diagnosis, addressing tasks such as detection, semantic segmentation, and classification for thyroid diseases and pediatric and adult brain tumors.

In summary, the outcomes of this thesis highlight the potential of deep learning-based methods for medical image analysis in the context of cancer diagnosis. These works demonstrate the versatility of deep learning in processing medical images from various sources and at different spatial resolutions and dimensionalities. Appropriate dataset curation, method validation and interpretation, and translational research are needed to promote the integration of deep learning-powered tools in the clinic.