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.

The aim of this study was address the challenges posed by the manual labeling of fetal ultrasound images by introducing an unsupervised approach, the fetal ultrasound semantic clustering (FUSC) method. The primary objective was to automatically cluster a large volume of ultrasound images into various fetal views, reducing or eliminating the need for labor-intensive manual labeling. The FUSC method was developed by using a substantial data set comprising 88,063 images. The methodology involves an unsupervised clustering approach to categorize ultrasound images into diverse fetal views. The method's effectiveness was further evaluated on an additional, unseen data set consisting of 8187 images. The evaluation included assessment of the clustering purity, and the entire process is detailed to provide insights into the method's performance. The FUSC method exhibited notable success, achieving >92% clustering purity on the evaluation data set of 8187 images. The results signify the feasibility of automatically clustering fetal ultrasound images without relying on manual labeling. The study showcases the potential of this approach in handling a large volume of ultrasound scans encountered in clinical practice, with implications for improving efficiency and accuracy in fetal ultrasound imaging. The findings of this investigation suggest that the FUSC method holds significant promise for the field of fetal ultrasound imaging. By automating the clustering of ultrasound images, this approach has the potential to reduce the manual labeling burden, making the process more efficient. The results pave the way for advanced automated labeling solutions, contributing to the enhancement of clinical practices in fetal ultrasound imaging. Our code is available at https://github.com/BioMedIA-MBZUAI/FUSC.

Cancer is one of the leading causes of death worldwide, and head and neck (H&N) cancer is amongst the most prevalent types. Positron emission tomography and computed tomography are used to detect, segment and quantify the tumor region. Clinically, tumor segmentation is extensively time-consuming and prone to error. Machine learning, and deep learning in particular, can assist to automate this process, yielding results as accurate as the results of a clinician. In this paper, we investigate a vision transformer-based method to automatically delineate H&N tumor, and compare its results to leading convolutional neural network (CNN)-based models. We use multi-modal data from CT and PET scans to perform the segmentation task. We show that a solution with a transformer-based model has the potential to achieve comparable results to CNN-based ones. With cross validation, the model achieves a mean dice similarity coefficient (DSC) of 0.736, mean precision of 0.766 and mean recall of 0.766. This is only 0.021 less than the 2020 competition winning model (cross validated in-house) in terms of the DSC score. On the testing set, the model performs similarly, with DSC of 0.736, precision of 0.773, and recall of 0.760, which is only 0.023 lower in DSC than the 2020 competition winning model. This work shows that cancer segmentation via transformer-based models is a promising research area to further explore.