Latest AI and machine learning research in pathology for healthcare professionals.
Oral squamous cell carcinoma (OSCC) is prevalent around the world and is associated with poor prognosis. OSCC is typically diagnosed from tissue biopsy sections by pathologists who rely on their empirical experience. Deep learning models may improve the accuracy and speed of image classification, thus reducing human error and workload. Here we developed a custom-made deep learning model to assist ...
Precision oncology relies on the identification of targetable molecular alterations in tumor tissues. In many tumor types, a limited set of molecular tests is currently part of standard diagnostic workflows. However, universal testing for all targetable alterations, especially rare ones, is limited by the cost and availability of molecular assays. From 2017 to 2021, multiple studies have shown tha...
The class distribution of a training dataset is an important factor which influences the performance of a deep learning-based system. Understanding th...
Diagnosis assignment is the process of assigning disease codes to patients. Automatic diagnosis assignment has the potential to validate code assignme...
Tissue biomanufacturing aims to produce lab-grown stem cell grafts and biomimetic drug testing platforms but remains limited in its ability to recapit...
BACKGROUND: Transplant nephropathology is a highly specialized field of pathology comprising both the evaluation of organ donor biopsy for organ alloc...
Two-photon fluorescence microscopy has enabled the three-dimensional (3D) neural imaging of deep cortical regions. While it can capture the detailed n...
Fifty-four slaughtered horses were classified into groups having adipose tissue in the crest of the neck with or without hemorrhage (AH and NH groups,...
Skin cancer is one of the most common diseases that can be initially detected by visual observation and further with the help of dermoscopic analysis ...
Neurosurgery is mainly for the treatment of head trauma, cerebrovascular disease, brain tumors, and spinal cord disorders. These operations are diffic...
BACKGROUND: Traditional histopathologic evaluation of peripheral nerve using brightfield microscopy is resource-intensive, necessitating complex sampl...
Automated classification and detection of brain abnormalities like a tumor(s) in reconstructed microwave (RMW) brain images are essential for medical ...
Combining experiments with artificial intelligence algorithms, we propose a machine learning based approach called wrinkle force microscopy (WFM) to e...
Hepatocellular carcinomas (HCCs) are aggressive tumors with a poor prognosis. Approved first-line treatments include sorafenib, lenvatinib, and a comb...
Segmenting three-dimensional (3D) microscopy images is essential for understanding phenomena like morphogenesis, cell division, cellular growth, and g...
BACKGROUND: Artificial intelligence using computer-aided diagnosis (CADx) in real time with images acquired during colonoscopy may help colonoscopists...
Background Deep learning (DL) algorithms could improve the classification of ovarian tumors assessed with multimodal US. Purpose To develop DL algorit...
Machine learning is rapidly becoming an integral part of experimental physical discovery via automated and high-throughput synthesis, and active exper...
Given the rapid developments, there is no doubt that artificial intelligence (AI) will substantially impact pathological diagnostics. However, it rema...
Fluorescence imaging is increasingly being implemented in surgery. One of the drawbacks of its application is the need to switch back-and-forth betwee...