AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Hematoxylin

Showing 51 to 60 of 85 articles

Clear Filters

Detection of malignant melanoma in H&E-stained images using deep learning techniques.

Tissue & cell
Histopathological images are widely used to diagnose diseases including skin cancer. As digital histopathological images are typically of very large size, in the order of several billion pixels, automated identification of all abnormal cell nuclei an...

Comparative analysis of machine learning approaches to classify tumor mutation burden in lung adenocarcinoma using histopathology images.

Scientific reports
Both histologic subtypes and tumor mutation burden (TMB) represent important biomarkers in lung cancer, with implications for patient prognosis and treatment decisions. Typically, TMB is evaluated by comprehensive genomic profiling but this requires ...

DLBCL-Morph: Morphological features computed using deep learning for an annotated digital DLBCL image set.

Scientific data
Diffuse Large B-Cell Lymphoma (DLBCL) is the most common non-Hodgkin lymphoma. Though histologically DLBCL shows varying morphologies, no morphologic features have been consistently demonstrated to correlate with prognosis. We present a morphologic a...

A method for utilizing automated machine learning for histopathological classification of testis based on Johnsen scores.

Scientific reports
We examined whether a tool for determining Johnsen scores automatically using artificial intelligence (AI) could be used in place of traditional Johnsen scoring to support pathologists' evaluations. Average precision, precision, and recall were asses...

A U-Net based framework to quantify glomerulosclerosis in digitized PAS and H&E stained human tissues.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Reliable counting of glomeruli and evaluation of glomerulosclerosis in renal specimens are essential steps to assess morphological changes in kidney and identify individuals requiring treatment. Because microscopic identification of sclerosed glomeru...

Deep Learning-Based Spermatogenic Staging Assessment for Hematoxylin and Eosin-Stained Sections of Rat Testes.

Toxicologic pathology
In preclinical toxicology studies, a "stage-aware" histopathological evaluation of testes is recognized as the most sensitive method to detect effects on spermatogenesis. A stage-aware evaluation requires the pathologist to be able to identify the di...

A deep learning diagnostic platform for diffuse large B-cell lymphoma with high accuracy across multiple hospitals.

Nature communications
Diagnostic histopathology is a gold standard for diagnosing hematopoietic malignancies. Pathologic diagnosis requires labor-intensive reading of a large number of tissue slides with high diagnostic accuracy equal or close to 100 percent to guide trea...

Piloting a Deep Learning Model for Predicting Nuclear BAP1 Immunohistochemical Expression of Uveal Melanoma from Hematoxylin-and-Eosin Sections.

Translational vision science & technology
BACKGROUND: Uveal melanoma (UM) is the most common primary intraocular malignancy in adults. Monosomy 3 and mutation are strong prognostic factors predicting metastatic risk in UM. Nuclear BAP1 (nBAP1) expression is a close immunohistochemical surro...

Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis.

Nature cancer
We use deep transfer learning to quantify histopathological patterns across 17,355 hematoxylin and eosin-stained histopathology slide images from 28 cancer types and correlate these with matched genomic, transcriptomic and survival data. This approac...