Pathology

Artificial intelligence in human in vitro fertilization and embryology.

Embryo evaluation and selection embody the aggregate manifestation of the entire in vitro fertilizat...

Reimagining T Staging Through Artificial Intelligence and Machine Learning Image Processing Approaches in Digital Pathology.

Tumor stage and grade, visually assessed by pathologists from evaluation of pathology images in conj...

Deep learning-level melanoma detection by interpretable machine learning and imaging biomarker cues.

SIGNIFICANCE: Melanoma is a deadly cancer that physicians struggle to diagnose early because they la...

Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning.

BACKGROUND: Identifying genetic mutations in cancer patients have been increasingly important becaus...

3D computational cannula fluorescence microscopy enabled by artificial neural networks.

Computational cannula microscopy (CCM) is a high-resolution widefield fluorescence imaging approach ...

Photonic artificial muscles: from micro robots to tissue engineering.

Light responsive shape-changing polymers are able to mimic the function of biological muscles accomp...

Deep Learning for Virtual Histological Staining of Bright-Field Microscopic Images of Unlabeled Carotid Artery Tissue.

PURPOSE: Histological analysis of artery tissue samples is a widely used method for diagnosis and qu...

Deep Learning Pre-training Strategy for Mammogram Image Classification: an Evaluation Study.

In this work, we assess how pre-training strategy affects deep learning performance for the task of ...

Ultrasonographic Thyroid Nodule Classification Using a Deep Convolutional Neural Network with Surgical Pathology.

Ultrasonography with fine-needle aspiration biopsy is commonly used to detect thyroid cancer. Howeve...

Construction of a convolutional neural network classifier developed by computed tomography images for pancreatic cancer diagnosis.

BACKGROUND: Efforts should be made to develop a deep-learning diagnosis system to distinguish pancre...

Rosiglitazone-induced changes in the oxidative stress metabolism and fatty acid composition in relation with trace element status in the primary adipocytes.

BACKGROUND: Metabolic syndrome, obesity and type 2 diabetes are metabolic disorders characterized by...

Digital cytology: Look how much has been achieved.

Anti-inflammatory and antibacterial activities of P. petiolosa (Christ) Ching ethyl acetate extract against S. aureus in mice.

P. petiolosa as a typical Chinese herbal medicine has been generally utilized as Chinese native medi...

Study on the differentially expressed genes and signaling pathways in dermatomyositis using integrated bioinformatics method.

Dermatomyositis is a common connective tissue disease. The occurrence and development of dermatomyos...

Segmentation of Masses on Mammograms Using Data Augmentation and Deep Learning.

The diagnosis of breast cancer in early stage is essential for successful treatment. Detection can b...

Deep learning links histology, molecular signatures and prognosis in cancer.

Deep learning can be used to predict genomic alterations based on morphological features learned fro...

Assessing the Accuracy of a Deep Learning Method to Risk Stratify Indeterminate Pulmonary Nodules.

The management of indeterminate pulmonary nodules (IPNs) remains challenging, resulting in invasive...

A New Classification of Benign, Premalignant, and Malignant Endometrial Tissues Using Machine Learning Applied to 1413 Candidate Variables.

Benign normal (NL), premalignant (endometrial intraepithelial neoplasia, EIN) and malignant (cancer,...

Explainable AI (xAI) for Anatomic Pathology.

Pathologists are adopting whole slide images (WSIs) for diagnosis, thanks to recent FDA approval of ...

Implementing Deep Learning Algorithms in Anatomic Pathology Using Open-source Deep Learning Libraries.

The application of artificial intelligence technologies to anatomic pathology has the potential to t...

Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study.

As many algorithms depend on a suitable representation of data, learning unique features is consider...