Pathology

Colorectal polyp characterization with endocytoscopy: Ready for widespread implementation with artificial intelligence?

Endocytoscopy provides an in-vivo visualization of nuclei and micro-vessels at the cellular level in real-time, facilitating so-called "optical biopsy" or "virtual histology" of colorectal polyps/neoplasms. This functionality is enabled by 520-fold magnification power with endocytoscopy and recent breakthroughs in artificial intelligence (AI) allowing a great advance in endocytoscopic imaging; int...

Denoising Autoencoder, A Deep Learning Algorithm, Aids the Identification of A Novel Molecular Signature of Lung Adenocarcinoma.

Precise biomarker development is a key step in disease management. However, most of the published biomarkers were derived from a relatively small number of samples with supervised approaches. Recent advances in unsupervised machine learning promise to leverage very large datasets for making better predictions of disease biomarkers. Denoising autoencoder (DA) is one of the unsupervised deep learnin...

The emerging role of deep learning in cytology.

Deep learning (DL) is a component or subset of artificial intelligence. DL has contributed significant change in feature extraction and image classifi...

Necrotic enteritis challenge regulates peroxisome proliferator-1 activated receptors signaling and β-oxidation pathways in broiler chickens.

Necrotic enteritis (NE) is an important enteric disease in poultry and has become a major concern in poultry production in the post-antibiotic era. Th...

ProsRegNet: A deep learning framework for registration of MRI and histopathology images of the prostate.

Magnetic resonance imaging (MRI) is an increasingly important tool for the diagnosis and treatment of prostate cancer. However, interpretation of MRI ...

Evidence of Gender Differences in the Diagnosis and Management of Coronavirus Disease 2019 Patients: An Analysis of Electronic Health Records Using Natural Language Processing and Machine Learning.

The impact of sex and gender in the incidence and severity of coronavirus disease 2019 (COVID-19) remains controversial. Here, we aim to describe the...

Artificial Intelligence in Thyroid Fine Needle Aspiration Biopsies.

BACKGROUND: From cell phones to aerospace, artificial intelligence (AI) has wide-reaching influence in the modern age. In this review, we discuss the ...

Semi-Supervised Classification of Noisy, Gigapixel Histology Images.

One of the greatest obstacles in the adoption of deep neural networks for new medical applications is that training these models typically require a l...

Texture features in the Shearlet domain for histopathological image classification.

BACKGROUND: A various number of imaging modalities are available (e.g., magnetic resonance, x-ray, ultrasound, and biopsy) where each modality can rev...

Deep learning extended depth-of-field microscope for fast and slide-free histology.

Microscopic evaluation of resected tissue plays a central role in the surgical management of cancer. Because optical microscopes have a limited depth-...

Effectiveness of transfer learning for enhancing tumor classification with a convolutional neural network on frozen sections.

Fast and accurate confirmation of metastasis on the frozen tissue section of intraoperative sentinel lymph node biopsy is an essential tool for critic...

In Situ Classification of Cell Types in Human Kidney Tissue Using 3D Nuclear Staining.

To understand the physiology and pathology of disease, capturing the heterogeneity of cell types within their tissue environment is fundamental. In su...

Deep learning powers cancer diagnosis in digital pathology.

Technological innovation has accelerated the pathological diagnostic process for cancer, especially in digitizing histopathology slides and incorporat...

Deep-learning-based multi-class segmentation for automated, non-invasive routine assessment of human pluripotent stem cell culture status.

Human induced pluripotent stem cells (hiPSCs) are capable of differentiating into a variety of human tissue cells. They offer new opportunities for pe...

Motion-flow-guided recurrent network for respiratory signal estimation of x-ray angiographic image sequences.

Motion compensation can eliminate inconsistencies of respiratory movement during image acquisitions for precise vascular reconstruction in the clinica...

Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images.

Histopathological images are a rich but incompletely explored data type for studying cancer. Manual inspection is time consuming, making it challengin...

[Enhanced imaging in urological endoscopy].

White light cystoscopy and the concise documentation of pathological findings are standard diagnostic procedures in urology. Additional imaging modali...

Fully automatic volume segmentation of infrarenal abdominal aortic aneurysm computed tomography images with deep learning approaches versus physician controlled manual segmentation.

OBJECTIVE: Imaging software has become critical tools in the diagnosis and decision making for the treatment of abdominal aortic aneurysms (AAA). Howe...

A large-scale internal validation study of unsupervised virtual trichrome staining technologies on nonalcoholic steatohepatitis liver biopsies.

Non-alcoholic steatohepatitis (NASH) is a fatty liver disease characterized by accumulation of fat in hepatocytes with concurrent inflammation and is ...

Single image super-resolution for whole slide image using convolutional neural networks and self-supervised color normalization.

High-quality whole slide scanners used for animal and human pathology scanning are expensive and can produce massive datasets, which limits the access...