Latest AI and machine learning research in pathology for healthcare professionals.
The efficient extraction of image data from curved tissue sheets embedded in volumetric imaging data remains a serious and unsolved problem in quantitative studies of embryogenesis. Here, we present DeepProjection (DP), a trainable projection algorithm based on deep learning. This algorithm is trained on user-generated training data to locally classify 3D stack content, and to rapidly and robustly...
Multiplexed immunofluorescence imaging allows the multidimensional molecular profiling of cellular environments at subcellular resolution. However, identifying and characterizing disease-relevant microenvironments from these rich datasets is challenging. Here we show that a graph neural network that leverages spatial protein profiles in tissue specimens to model tumour microenvironments as local s...
Non-alcoholic fatty liver disease (NAFLD) affects about 24% of the world's population. Progression of early stages of NAFLD can lead to the more advan...
The implementation of robotic systems for minimally invasive surgery and medical procedures is an active topic of research in recent years. One of the...
Although still in its infancy, artificial intelligence (AI) analysis of kidney biopsy images is anticipated to become an integral aspect of renal hist...
Digital pathology coupled with advanced machine learning (e.g., deep learning) has been changing the paradigm of whole-slide histopathological images ...
Organoids have immense potential as ex vivo disease models for drug discovery and personalized drug screening. Dynamic changes in individual organoid ...
RATIONALE AND OBJECTIVES: Programmed Death-Ligand 1 (PD-L1) is an important biomarker for patient selection of immunotherapy in gastric cancer (GC). T...
The purpose of this study was to realize an automated volume measurement of abdominal adipose tissue from the entire abdominal cavity in Dixon magneti...
Spatial metabolomics describes the spatially resolved analysis of interconnected pathways, biochemical reactions, and transport processes of small mol...
During the past 10 years, biological electron cryo-microscopy (cryoEM) has undergone a process of rapid transformation. Many things we could only drea...
Programmed death ligand-1 (PD-L1) has been recently adopted for breast cancer as a predictive biomarker for immunotherapies. The cost, time, and varia...
There are currently >1.3 million human -omics samples that are publicly available. This valuable resource remains acutely underused because discoverin...
The purpose of this study was to develop a deep learning model to diagnose breast cancer by embedding a diagnostic algorithm that examines the asymmet...
Recent development of imaging flow cytometry (IFC) has enabled the measurements of single cells with high throughput, where fluorescent labels provide...
PURPOSE: Rapid diagnosis and risk stratification can provide timely treatment for colorectal cancer (CRC) patients. Deep learning (DL) is not only use...
Cerebrovascular disease refers to damage to brain tissue caused by impaired intracranial blood circulation. It usually presents clinically as an acute...
Computer vision as a subcategory of deep learning tackles complex vision tasks by dealing with data of images. Molecular images with exceptionally hig...
Phenotypic plasticity is essential to the immune system, yet the factors that shape it are not fully understood. Here, we comprehensively analyze immu...
A model's ability to express its own predictive uncertainty is an essential attribute for maintaining clinical user confidence as computational biomar...