Pathology

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. However, because current clinically accepted methods are invasive, its monitoring is limited to patients in critical conditions. On the other hand, there are other less critical conditions for which ICP monitoring could still be useful; therefore, there is a need to develop non-invasive methods. We propos...

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

Classification of HEp-2 cell patterns plays a significant role in the indirect immunofluorescence test for identifying autoimmune diseases in the human body. Many automatic HEp-2 cell classification methods have been proposed in recent years, amongst which deep learning based methods have shown impressive performance. This paper provides a comprehensive review of the existing deep learning based H...

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 patients with clinically negative axillary lymph node...

Improving the accuracy of gastrointestinal neuroendocrine tumor grading with deep learning.

The Ki-67 index is an established prognostic factor in gastrointestinal neuroendocrine tumors (GI-NETs) and defines tumor grade. It is currently estim...

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 acknowledged tool to investigate cell behaviour in a tissue-l...

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 needle aspiration and touch preparations of small ...

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 decision. In this study, we attempt to use 3D deep...

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 background in over one-third of patients. Mutations in...

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 essential task for the accurate staging of breast can...

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 neurosurgeon to consider biopsy or surgical resection. Intrao...

Deep learning approach to describe and classify fungi microscopic images.

Preliminary diagnosis of fungal infections can rely on microscopic examination. However, in many cases, it does not allow unambiguous identification o...

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

Multiparametric magnetic resonance imaging (mpMRI) has been shown to improve radiologists' performance in the clinical diagnosis of breast cancer. Thi...

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 their important applications is the development o...

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-methylguanosine (1-MG), and 8-hydroxy-2' deoxyguanos...

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 prognostication. Machine learning and particularly d...

Interactive thyroid whole slide image diagnostic system using deep representation.

BACKGROUND AND OBJECTIVES: The vast size of the histopathology whole slide image poses formidable challenges to its automatic diagnosis. With the goal...

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 typically rely on personal experience. A CAD syst...

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 generate new therapeutic and biomarker hypotheses. We in...

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, making it possible to systematically link the morpho...

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 diagnosis and treatment, such as guiding biopsy procedure ...