Pathology

DeepRegularizer: Rapid Resolution Enhancement of Tomographic Imaging Using Deep Learning.

Optical diffraction tomography measures the three-dimensional refractive index map of a specimen and visualizes biochemical phenomena at the nanoscale in a non-destructive manner. One major drawback of optical diffraction tomography is poor axial resolution due to limited access to the three-dimensional optical transfer function. This missing cone problem has been addressed through regularization ...

Digital/Computational Technology for Molecular Cytology Testing: A Short Technical Note with Literature Review.

This short article describes the method of digital cytopathology using Z-stack scanning with or without extended focusing. This technology is suitable to observe such thick clusters as adenocarcinoma on cytologic specimens. Artificial intelligence (AI) has been applied to histological images, but its application on cytologic images is still limited. This article describes our attempt to apply AI t...

Improving DCIS diagnosis and predictive outcome by applying artificial intelligence.

Breast ductal carcinoma in situ (DCIS) is a preinvasive lesion that is considered to be a precursor to invasive breast cancer. Nevertheless, not all D...

Histopathological characteristics and artificial intelligence for predicting tumor mutational burden-high colorectal cancer.

BACKGROUND: Tumor mutational burden-high (TMB-H), which is detected with gene panel testing, is a promising biomarker for immune checkpoint inhibitors...

Real-time intraoperative glioma diagnosis using fluorescence imaging and deep convolutional neural networks.

PURPOSE: Surgery is the predominant treatment modality of human glioma but suffers difficulty on clearly identifying tumor boundaries in clinic. Conve...

Machine learning for the prediction of pathologic pneumatosis intestinalis.

BACKGROUND: The radiographic finding of pneumatosis intestinalis can indicate a spectrum of underlying processes ranging from a benign finding to a li...

A through-focus scanning optical microscopy dimensional measurement method based on deep-learning classification model.

Through-focus scanning optical microscopy (TSOM) is an economical, non-contact and nondestructive method for rapid measurement of three-dimensional na...

Feasibility of deep learning-based fully automated classification of microsatellite instability in tissue slides of colorectal cancer.

High levels of microsatellite instability (MSI-H) occurs in about 15% of sporadic colorectal cancer (CRC) and is an important predictive marker for re...

Independent real-world application of a clinical-grade automated prostate cancer detection system.

Artificial intelligence (AI)-based systems applied to histopathology whole-slide images have the potential to improve patient care through mitigation ...

Cheetah: A Computational Toolkit for Cybergenetic Control.

Advances in microscopy, microfluidics, and optogenetics enable single-cell monitoring and environmental regulation and offer the means to control cell...

Machine-learning based prediction of Cushing's syndrome in dogs attending UK primary-care veterinary practice.

Cushing's syndrome is an endocrine disease in dogs that negatively impacts upon the quality-of-life of affected animals. Cushing's syndrome can be a c...

Imaging and liquid biopsy in the prediction and evaluation of response to PRRT in neuroendocrine tumors: implications for patient management.

PURPOSE: The aim of this narrative review is to give an overview on current and emerging imaging methods and liquid biopsy for prediction and evaluati...

Automated biomarker candidate discovery in imaging mass spectrometry data through spatially localized Shapley additive explanations.

The search for molecular species that are differentially expressed between biological states is an important step towards discovering promising biomar...

Multi-magnification-based machine learning as an ancillary tool for the pathologic assessment of shaved margins for breast carcinoma lumpectomy specimens.

The surgical margin status of breast lumpectomy specimens for invasive carcinoma and ductal carcinoma in situ (DCIS) guides clinical decisions, as pos...

Improving axial resolution in Structured Illumination Microscopy using deep learning.

Structured Illumination Microscopy (SIM) is a widespread methodology to image live and fixed biological structures smaller than the diffraction limits...

Development and initial validation of a deep learning algorithm to quantify histological features in colorectal carcinoma including tumour budding/poorly differentiated clusters.

AIMS: To develop and validate a deep learning algorithm to quantify a broad spectrum of histological features in colorectal carcinoma.

Automated EEG pathology detection based on different convolutional neural network models: Deep learning approach.

The brain electrical activity, recorded and materialized as electroencephalogram (EEG) signals, is known to be very useful in the diagnosis of brain-r...

Senhance surgical system in benign hysterectomy: A real-world comparative assessment of case times and instrument costs versus da Vinci robotics and laparoscopic-assisted vaginal hysterectomy procedures.

OBJECTIVES: Comparison of retrospective, learning curve benign hysterectomy cost and case time data from Senhance total laparoscopic hysterectomy (TLH...

Deep Learning-Based Denoising in High-Speed Portable Reflectance Confocal Microscopy.

BACKGROUND AND OBJECTIVE: Portable confocal microscopy (PCM) is a low-cost reflectance confocal microscopy technique that can visualize cellular detai...

Machine-Learning-Based Evaluation of Intratumoral Heterogeneity and Tumor-Stroma Interface for Clinical Guidance.

Assessment of intratumoral heterogeneity and tumor-host interaction within the tumor microenvironment is becoming increasingly important for innovativ...