The number of studies on deep learning for medical diagnosis is expanding, and these systems are often claimed to outperform clinicians. However, only a few systems have shown medical efficacy. From this perspective, we examine a wide range of deep learning algorithms for the assessment of glioblastoma - a common brain tumor in older adults that is lethal. Surgery, chemotherapy, and radiation are the standard treatments for glioblastoma patients. The methylation status of the MGMT promoter, a specific genetic sequence found in the tumor, affects chemotherapy's effectiveness. MGMT promoter methylation improves chemotherapy response and survival in several cancers. MGMT promoter methylation is determined by a tumor tissue biopsy, which is then genetically tested. This lengthy and invasive procedure increases the risk of infection and other complications. Thus, researchers have used deep learning models to examine the tumor from brain MRI scans to determine the MGMT promoter's methylation state. We employ deep learning models and one of the largest public MRI datasets of 585 participants to predict the methylation status of the MGMT promoter in glioblastoma tumors using MRI scans. We test these models using Grad-CAM, occlusion sensitivity, feature visualizations, and training loss landscapes. Our results show no correlation between these two, indicating that external cohort data should be used to verify these models' performance to assure the accuracy and reliability of deep learning systems in cancer diagnosis.

Deep Learning has shown great success in reshaping medical imaging, yet it faces several challenges hindering widespread application. Distribution shifts in the continuously evolving data stream and catastrophic forgetting are two barriers that considerably increase the performance gap between research and applications. Continual Learning offers promise in addressing these hurdles by enabling the sequential acquisition of new knowledge without forgetting previous information. In this survey, we comprehensively review the recent literature on continual learning in the medical domain, highlight recent trends, and point out several practical issues. Specifically, we survey the continual learning studies on classification, segmentation, detection, and other related tasks in the medical domain and develop a taxonomy for the reviewed studies. We also critically discuss the current state of continual learning in medical imaging, including identifying open problems and outlining promising future directions. We hope that this survey provides researchers with a useful overview of the developments in the field and further increases engagement with this topic within the community. To keep up with the fast-paced advancements in the field, we will routinely update the repository with the latest relevant articles at https://github.com/BioMedIA-MBZUAI/awesome-cl-in-medical .