Latest AI and machine learning research in pathology for healthcare professionals.
BACKGROUND: Automated text classification has many important applications in the clinical setting; however, obtaining labelled data for training machine learning and deep learning models is often difficult and expensive. Active learning techniques may mitigate this challenge by reducing the amount of labelled data required to effectively train a model. In this study, we analyze the effectiveness o...
This work addresses the fabrication of an efficient, novel, and economically viable immunosensing armamentarium that will detect the carcinoembryonic antigen (CEA) typically associated with solid tumors (sarcomas, carcinomas, and lymphomas) and is used as a clinical tumor marker for all these malignancies. We synthesized silver nanoparticles by single-step chemical reduction and coated with silica...
Whole slide imaging, which is an important technique in the field of digital pathology, has recently been the subject of increased interest and avenue...
Tissue micro-morphological abnormalities and interrelated quantitative data can provide immediate evidences for tumorigenesis and metastasis in microe...
The tumor proliferation, which is correlated with tumor grade, is a crucial biomarker indicative of breast cancer patients' prognosis. The most common...
OBJECTIVES: To develop a lightweight deep convolutional neural network (CNN) for binary classification of oral lesions into benign and malignant or po...
The treatment plan of colorectal neoplasm differs based on histology. Although new endoscopic imaging systems have been developed, there are clear dia...
BACKGROUND: We present a novel robotic neuronavigation system (RONNA G4), used for precise preoperative planning and frameless neuronavigation, develo...
OBJECTIVES: This study aims to develop a computer-aided diagnosis (CADx) scheme to classify between benign and malignant ground glass nodules (GGNs), ...
Trastuzumab () is useful in the clinical management of HER2-positive metastatic breast, gastric, and colorectal carcinoma but has been limited by its ...
We introduce HistoNet, a deep neural network trained on normal tissue. On 1690 slides with rat tissue samples from 6 preclinical toxicology studies, t...
BACKGROUND: Basal cell carcinoma (BCC) is the most common type of skin cancer with incidence rates rising each year. Mohs micrographic surgery (MMS) i...
Probe-based confocal laser endomicroscopy (pCLE) allows for real-time diagnosis of dysplasia and cancer in Barrett's esophagus (BE) but is limited by ...
Ultrasound localization microscopy has enabled super-resolution vascular imaging through precise localization of individual ultrasound contrast agents...
We present DeepMIB, a new software package that is capable of training convolutional neural networks for segmentation of multidimensional microscopy d...
BACKGROUND: There are no established accurate models that use machine learning (ML) methods to preoperatively predict immediate remission after transs...
Deep-learning methods for computational pathology require either manual annotation of gigapixel whole-slide images (WSIs) or large datasets of WSIs wi...
IMPORTANCE: Cervical cancer is highly preventable but remains a common and deadly cancer in areas without screening programs. The creation of a diagno...
Rolling bearing fault diagnosis is one of the challenging tasks and hot research topics in the condition monitoring and fault diagnosis of rotating ma...
BACKGROUND: Prostate volume, as determined by magnetic resonance imaging (MRI), is a useful biomarker both for distinguishing between benign and malig...