Glioma is the most pernicious cancer of the nervous system, with histological grade influencing the survival of patients. Despite many studies on the multimodal treatment approach, survival time remains brief. In this study, a novel two-stage ensembl...
Diagnosis and Gleason grading of prostate cancer in biopsies are critical for the clinical management of men with prostate cancer. Despite this, the high grading variability among pathologists leads to the potential for under- and overtreatment. Arti...
Convolutional neural networks (CNNs) are state-of-the-art computer vision techniques for various tasks, particularly for image classification. However, there are domains where the training of classification models that generalize on several datasets ...
Virchows Archiv : an international journal of pathology
Jul 1, 2021
Histologic grading has been a simple and inexpensive method to assess tumor behavior and prognosis of invasive breast cancer grading, thereby identifying patients at risk for adverse outcomes, who may be eligible for (neo)adjuvant therapies. Histolog...
OBJECTIVES: To compare diagnostic performance of British Thyroid Association (BTA), American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS) and Artificial Intelligence TIRADS (AI-TIRADS) for thyroid nodule malignancy. To ...
Bladder cancer is one of the top 10 frequently occurring cancers and leads to most cancer deaths worldwide. Recently, blue light (BL) cystoscopy-based photodynamic diagnosis was introduced as a unique technology to enhance the detection of bladder ca...
BACKGROUND: Efforts are ongoing to try and find ways to reduce the number of unnecessary prostate biopsies without missing clinically significant prostate cancers (csPCa). The utility of multiparametric magnetic resonance imaging (mpMRI) in detecting...
BACKGROUND: One challenge to train deep convolutional neural network (CNNs) models with whole slide images (WSIs) is providing the required large number of costly, manually annotated image regions. Strategies to alleviate the scarcity of annotated da...
AIM: Artificial intelligence (AI)-based breast cancer grading may help to overcome perceived limitations of human assessment. Here, the potential value of AI grade was evaluated at the molecular level and in predicting patient outcome.
OBJECTIVE: To develop a new digital biomarker based on the analysis of primary tumour tissue by a convolutional neural network (CNN) to predict lymph node metastasis (LNM) in a cohort matched for already established risk factors.
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.