Pathology

Segmentation aware probabilistic phenotyping of single-cell spatial protein expression data.

Spatial protein expression technologies can map cellular content and organization by simultaneously quantifying the expression of >40 proteins at subcellular resolution within intact tissue sections and cell lines. However, necessary image segmentation to single cells is challenging and error prone, easily confounding the interpretation of cellular phenotypes and cell clusters. To address these li...

Self-interactive learning: Fusion and evolution of multi-scale histomorphology features for molecular traits prediction in computational pathology.

Predicting disease-related molecular traits from histomorphology brings great opportunities for precision medicine. Despite the rich information present in histopathological images, extracting fine-grained molecular features from standard whole slide images (WSI) is non-trivial. The task is further complicated by the lack of annotations for subtyping and contextual histomorphological features that...

Transcriptomic profiling and machine learning reveal novel RNA signatures for enhanced molecular characterization of Hashimoto's thyroiditis.

While ultrasonography effectively diagnoses Hashimoto's thyroiditis (HT), exploring its transcriptomic landscape could reveal valuable insights into d...

Evaluation of an enhanced ResNet-18 classification model for rapid On-site diagnosis in respiratory cytology.

OBJECTIVE: Rapid on-site evaluation (ROSE) of respiratory cytology specimens is a critical technique for accurate and timely diagnosis of lung cancer....

Automated CAD system for early detection and classification of pancreatic cancer using deep learning model.

Accurate diagnosis of pancreatic cancer using CT scan images is critical for early detection and treatment, potentially saving numerous lives globally...

Radiomics and Deep Learning Model for Benign and Malignant Soft Tissue Tumors Differentiation of Extremities and Trunk.

RATIONALE AND OBJECTIVES: To develop radiomics and deep learning models for differentiating malignant and benign soft tissue tumors (STTs) preoperativ...

Biological contaminants analysis in microalgae culture by UV-vis spectroscopy and machine learning.

This study elucidates the utility and efficacy of UV-visible spectroscopy for the detection and characterization of biological contaminants within mic...

Varying pixel resolution significantly improves deep learning-based carotid plaque histology segmentation.

Carotid plaques-the buildup of cholesterol, calcium, cellular debris, and fibrous tissues in carotid arteries-can rupture, release microemboli into th...

Fibrosis and inflammatory activity diagnosis of chronic hepatitis C based on extreme learning machine.

The traditional diagnosis of chronic hepatitis C usually relies on liver biopsy. Diagnosing chronic hepatitis C based on serum indices provides a non-...

Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy.

Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learnin...

Weakly supervised deep learning-based classification for histopathology of gliomas: a single center experience.

Multiple artificial intelligence systems have been created to facilitate accurate and prompt histopathological diagnosis of tumors using hematoxylin-e...

Deciphering the cellular and molecular landscape of pulmonary fibrosis through single-cell sequencing and machine learning.

Pulmonary fibrosis is characterized by progressive lung scarring, leading to a decline in lung function and an increase in morbidity and mortality. Th...

Spatially-Constrained and -Unconstrained Bi-Graph Interaction Network for Multi-Organ Pathology Image Classification.

In computational pathology, graphs have shown to be promising for pathology image analysis. There exist various graph structures that can discover dif...

Unsupervised Non-Rigid Histological Image Registration Guided by Keypoint Correspondences Based on Learnable Deep Features With Iterative Training.

Histological image registration is a fundamental task in histological image analysis. It is challenging because of substantial appearance differences ...

Self-Supervised Representation Distribution Learning for Reliable Data Augmentation in Histopathology WSI Classification.

Multiple instance learning (MIL) based whole slide image (WSI) classification is often carried out on the representations of patches extracted from WS...

Artificial Intelligence and Whole Slide Imaging Assist in Thyroid Indeterminate Cytology: A Systematic Review.

INTRODUCTION: Thyroid cytopathology, particularly in cases of atypia of undetermined significance/follicular lesions of undetermined significance (AUS...

HistoColAi: An open-source web platform for collaborative digital histology image annotation with AI-driven predictive integration.

Digital pathology is now a standard component of the pathology workflow, offering numerous benefits such as high-detail whole slide images and the cap...

Deep learning-enabled filter-free fluorescence microscope.

Optical filtering is an indispensable part of fluorescence microscopy for selectively highlighting molecules labeled with a specific fluorophore and s...

Identification of common diagnostic genes and molecular pathways in endometriosis and systemic lupus erythematosus by machine learning approach and in vitro experiment.

Growing research suggests that endometriosis and systemic lupus erythematosus (SLE) are both chronic inflammatory diseases and closely related, but no...

Machine learning identifies remodeling patterns in human lung extracellular matrix.

Organ function depends on the three-dimensional integrity of the extracellular matrix (ECM). The structure resulting from the location and association...