AI Medical Compendium Journal:
Laboratory investigation; a journal of technical methods and pathology

Showing 21 to 30 of 40 articles

Interpretable tumor differentiation grade and microsatellite instability recognition in gastric cancer using deep learning.

Laboratory investigation; a journal of technical methods and pathology
Gastric cancer possesses great histological and molecular diversity, which creates obstacles for rapid and efficient diagnoses. Classic diagnoses either depend on the pathologist's judgment, which relies heavily on subjective experience, or time-cons...

Artificial intelligence modelling in differentiating core biopsies of fibroadenoma from phyllodes tumor.

Laboratory investigation; a journal of technical methods and pathology
Breast fibroepithelial lesions (FEL) are biphasic tumors which consist of benign fibroadenomas (FAs) and the rarer phyllodes tumors (PTs). FAs and PTs have overlapping features, but have different clinical management, which makes correct core biopsy ...

Predicting 1p/19q co-deletion status from magnetic resonance imaging using deep learning in adult-type diffuse lower-grade gliomas: a discovery and validation study.

Laboratory investigation; a journal of technical methods and pathology
Determination of 1p/19q co-deletion status is important for the classification, prognostication, and personalized therapy in diffuse lower-grade gliomas (LGG). We developed and validated a deep learning imaging signature (DLIS) from preoperative magn...

Deep convolutional neural network-based algorithm for muscle biopsy diagnosis.

Laboratory investigation; a journal of technical methods and pathology
Histopathologic evaluation of muscle biopsy samples is essential for classifying and diagnosing muscle diseases. However, the numbers of experienced specialists and pathologists are limited. Although new technologies such as artificial intelligence a...

An active learning approach for clustering single-cell RNA-seq data.

Laboratory investigation; a journal of technical methods and pathology
Single-cell RNA sequencing (scRNA-seq) data has been widely used to profile cellular heterogeneities with a high-resolution picture. Clustering analysis is a crucial step of scRNA-seq data analysis because it provides a chance to identify and uncover...

Quantitative assessment of inflammatory infiltrates in kidney transplant biopsies using multiplex tyramide signal amplification and deep learning.

Laboratory investigation; a journal of technical methods and pathology
Delayed graft function (DGF) is a strong risk factor for development of interstitial fibrosis and tubular atrophy (IFTA) in kidney transplants. Quantitative assessment of inflammatory infiltrates in kidney biopsies of DGF patients can reveal predicti...

Exploring photoacoustic spectroscopy-based machine learning together with metabolomics to assess breast tumor progression in a xenograft model ex vivo.

Laboratory investigation; a journal of technical methods and pathology
In the current study, a breast tumor xenograft was established in athymic nude mice by subcutaneous injection of the MCF-7 cell line and assessed the tumor progression by photoacoustic spectroscopy combined with machine learning tools. The advancemen...

GestAltNet: aggregation and attention to improve deep learning of gestational age from placental whole-slide images.

Laboratory investigation; a journal of technical methods and pathology
The placenta is the first organ to form and performs the functions of the lung, gut, kidney, and endocrine systems. Abnormalities in the placenta cause or reflect most abnormalities in gestation and can have life-long consequences for the mother and ...

Deep learning-based grading of ductal carcinoma in situ in breast histopathology images.

Laboratory investigation; a journal of technical methods and pathology
Ductal carcinoma in situ (DCIS) is a non-invasive breast cancer that can progress into invasive ductal carcinoma (IDC). Studies suggest DCIS is often overtreated since a considerable part of DCIS lesions may never progress into IDC. Lower grade lesio...

Performance and efficiency of machine learning algorithms for analyzing rectangular biomedical data.

Laboratory investigation; a journal of technical methods and pathology
Most biomedical datasets, including those of 'omics, population studies, and surveys, are rectangular in shape and have few missing data. Recently, their sample sizes have grown significantly. Rigorous analyses on these large datasets demand consider...