Pathology

Computational Technology with Artificial Intelligence and Machine Learning: What Should a Cytologist Do with It?

Identifying Fatal Head Injuries on Postmortem Computed Tomography Using Convolutional Neural Network/Deep Learning: A Feasibility Study.

Postmortem computed tomography (PMCT) is a relatively recent advancement in forensic pathology pract...

Non-Invasive Estimation of Intracranial Pressure by Diffuse Optics: A Proof-of-Concept Study.

Intracranial pressure (ICP) is an important parameter to monitor in several neuropathologies. Howeve...

Deep learning based HEp-2 image classification: A comprehensive review.

Classification of HEp-2 cell patterns plays a significant role in the indirect immunofluorescence te...

New technologies in breast cancer sentinel lymph node biopsy; from the current gold standard to artificial intelligence.

Sentinel lymph node biopsy is an important diagnostic procedure performed in early breast cancer pat...

Automated spheroid generation, drug application and efficacy screening using a deep learning classification: a feasibility study.

The last two decades saw the establishment of three-dimensional (3D) cell cultures as an acknowledge...

Review of different platforms to perform rapid onsite evaluation via telecytology.

There is increased utilisation of cytopathology to provide a rapid onsite evaluation (ROSE) of fine ...

Histological Subtypes Classification of Lung Cancers on CT Images Using 3D Deep Learning and Radiomics.

RATIONALE AND OBJECTIVES: Histological subtypes of lung cancers are critical for clinical treatment ...

Metabolomics, machine learning and immunohistochemistry to predict succinate dehydrogenase mutational status in phaeochromocytomas and paragangliomas.

Phaeochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumours with a hereditary back...

Challenge for Diagnostic Assessment of Deep Learning Algorithm for Metastases Classification in Sentinel Lymph Nodes on Frozen Tissue Section Digital Slides in Women with Breast Cancer.

PURPOSE: Assessing the status of metastasis in sentinel lymph nodes (SLNs) by pathologists is an ess...

Automated histologic diagnosis of CNS tumors with machine learning.

The discovery of a new mass involving the brain or spine typically prompts referral to a neurosurgeo...

Deep learning approach to describe and classify fungi microscopic images.

Preliminary diagnosis of fungal infections can rely on microscopic examination. However, in many cas...

A deep learning methodology for improved breast cancer diagnosis using multiparametric MRI.

Multiparametric magnetic resonance imaging (mpMRI) has been shown to improve radiologists' performan...

CellCountCV-A Web-Application for Accurate Cell Counting and Automated Batch Processing of Microscopic Images Using Fully Convolutional Neural Networks.

In vitro cellular models are promising tools for studying normal and pathological conditions. One of...

Study on Urinary Candidate Metabolome for the Early Detection of Breast Cancer.

A metabolomic study for determination of certain urinary metabolomes, 1-methyladenosine (1-MA), 1-me...

Hypothesis-free deep survival learning applied to the tumour microenvironment in gastric cancer.

The biological complexity reflected in histology images requires advanced approaches for unbiased pr...

Interactive thyroid whole slide image diagnostic system using deep representation.

BACKGROUND AND OBJECTIVES: The vast size of the histopathology whole slide image poses formidable ch...

Evaluation of a deep learning-based computer-aided diagnosis system for distinguishing benign from malignant thyroid nodules in ultrasound images.

PURPOSE: Computer-aided diagnosis (CAD) systems assist in solving subjective diagnosis problems that...

Unsupervised machine learning reveals risk stratifying glioblastoma tumor cells.

A goal of cancer research is to reveal cell subsets linked to continuous clinical outcomes to genera...

Integrating spatial gene expression and breast tumour morphology via deep learning.

Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, ma...

Superpixel-based deep convolutional neural networks and active contour model for automatic prostate segmentation on 3D MRI scans.

Automatic and reliable prostate segmentation is an essential prerequisite for assisting the diagnosi...