Pathology

Methods for correcting inference based on outcomes predicted by machine learning.

Many modern problems in medicine and public health leverage machine-learning methods to predict outcomes based on observable covariates. In a wide array of settings, predicted outcomes are used in subsequent statistical analysis, often without accounting for the distinction between observed and predicted outcomes. We call inference with predicted outcomes postprediction inference. In this paper, w...

Risk prediction for malignant intraductal papillary mucinous neoplasm of the pancreas: logistic regression versus machine learning.

Most models for predicting malignant pancreatic intraductal papillary mucinous neoplasms were developed based on logistic regression (LR) analysis. Our study aimed to develop risk prediction models using machine learning (ML) and LR techniques and compare their performances. This was a multinational, multi-institutional, retrospective study. Clinical variables including age, sex, main duct diamete...

Renal function in children infected with : a case-control study of an endemic Ghanaian community.

Schistosomiasis has been associated with kidney diseases leading to serious health problems especially in advanced cases. Most studies have used renal...

Screening of important metabolites and KRAS genotypes in colon cancer using secondary ion mass spectrometry.

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) is an imaging-based analytical technique that can characterize the surfaces of biomaterials....

Improved automatic detection of herpesvirus secondary envelopment stages in electron microscopy by augmenting training data with synthetic labelled images generated by a generative adversarial network.

Detailed analysis of secondary envelopment of the herpesvirus human cytomegalovirus (HCMV) by transmission electron microscopy (TEM) is crucial for un...

Assessing hERG1 Blockade from Bayesian Machine-Learning-Optimized Site Identification by Ligand Competitive Saturation Simulations.

Drug-induced cardiotoxicity is a potentially lethal and yet one of the most common side effects with the drugs in clinical use. Most of the drug-induc...

Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains.

For newly diagnosed breast cancer, estrogen receptor status (ERS) is a key molecular marker used for prognosis and treatment decisions. During clinica...

Thyroid nodules risk stratification through deep learning based on ultrasound images.

PURPOSE: Clinically, the risk stratification of thyroid nodules is usually used to formulate the next treatment plan. The American College of Radiolog...

Artificial neural networks and pathologists recognize basal cell carcinomas based on different histological patterns.

Recent advances in artificial intelligence, particularly in the field of deep learning, have enabled researchers to create compelling algorithms for m...

Risks of Muscle Atrophy in Patients with Malignant Lymphoma after Autologous Stem Cell Transplantation.

OBJECTIVE: Muscle atrophy is associated with autologous stem cell transplantation (ASCT)-related outcomes in patients with malignant lymphoma (ML). Ho...

Image-based phenotyping of disaggregated cells using deep learning.

The ability to phenotype cells is fundamentally important in biological research and medicine. Current methods rely primarily on fluorescence labeling...

Predicting lymph node metastasis in patients with oropharyngeal cancer by using a convolutional neural network with associated epistemic and aleatoric uncertainty.

There can be significant uncertainty when identifying cervical lymph node (LN) metastases in patients with oropharyngeal squamous cell carcinoma (OPSC...

A convolutional neural network segments yeast microscopy images with high accuracy.

The identification of cell borders ('segmentation') in microscopy images constitutes a bottleneck for large-scale experiments. For the model organism ...

Deeply-supervised density regression for automatic cell counting in microscopy images.

Accurately counting the number of cells in microscopy images is required in many medical diagnosis and biological studies. This task is tedious, time-...

Machine learning assisted intraoperative assessment of brain tumor margins using HRMAS NMR spectroscopy.

Complete resection of the tumor is important for survival in glioma patients. Even if the gross total resection was achieved, left-over micro-scale ti...

Metabolomics of Prostate Cancer Gleason Score in Tumor Tissue and Serum.

Gleason score, a measure of prostate tumor differentiation, is the strongest predictor of lethal prostate cancer at the time of diagnosis. Metabolomic...

Diagnostic accuracy of deep-learning with anomaly detection for a small amount of imbalanced data: discriminating malignant parotid tumors in MRI.

We hypothesized that, in discrimination between benign and malignant parotid gland tumors, high diagnostic accuracy could be obtained with a small amo...

Are Silver Nanoparticles Useful for Treating Second-Degree Burns? An Experimental Study in Rats.

In this work, the potential usefulness of silver nanoparticles (AgNPs) for treating burn wounds was examined. Second-degree burns were induced in ma...

A convolutional neural network-based learning approach to acute lymphoblastic leukaemia detection with automated feature extraction.

Leukaemia is a type of blood cancer which mainly occurs when bone marrow produces excess white blood cells in our body. This disease not only affects ...

Deep Learning-Based Segmentation and Quantification in Experimental Kidney Histopathology.

BACKGROUND: Nephropathologic analyses provide important outcomes-related data in experiments with the animal models that are essential for understandi...