Pathology

Data-driven translational prostate cancer research: from biomarker discovery to clinical decision.

Prostate cancer (PCa) is a common malignant tumor with increasing incidence and high heterogeneity among males worldwide. In the era of big data and artificial intelligence, the paradigm of biomarker discovery is shifting from traditional experimental and small data-based identification toward big data-driven and systems-level screening. Complex interactions between genetic factors and environment...

Transoral robotic narrow field oropharyngectomy for tumours of the parapharyngeal space.

The advent of transoral robotic surgery (TORS) has allowed transoral approaches for parapharyngeal space (PPS) tumours to be re-evaluated. It provides enhanced visualisation and instrument access for appropriate tumours. We describe a specific technique, TORS narrow-field oropharyngectomy, that is ideal for benign PPS tumours which have been violated by intra-oral biopsy or incision and drainage. ...

Artificial intelligence and convolution neural networks assessing mammographic images: a narrative literature review.

Studies have shown that the use of artificial intelligence can reduce errors in medical image assessment. The diagnosis of breast cancer is an essenti...

Deep learning approach to classification of lung cytological images: Two-step training using actual and synthesized images by progressive growing of generative adversarial networks.

Cytology is the first pathological examination performed in the diagnosis of lung cancer. In our previous study, we introduced a deep convolutional ne...

The impact of iterative reconstruction algorithms on machine learning-based coronary CT angiography-derived fractional flow reserve (CT-FFR) values.

To evaluate the impact of an iterative reconstruction (IR) algorithm (advanced modeled iterative reconstruction, ADMIRE) on machine learning-based cor...

Comparing Deep Learning Models for Multi-cell Classification in Liquid- based Cervical Cytology Image.

Liquid-based cytology (LBC) is a reliable automated technique for the screening of Papanicolaou (Pap) smear data. It is an effective technique for col...

Artificial intelligence as the next step towards precision pathology.

Pathology is the cornerstone of cancer care. The need for accuracy in histopathologic diagnosis of cancer is increasing as personalized cancer therapy...

DeepSurvNet: deep survival convolutional network for brain cancer survival rate classification based on histopathological images.

Histopathological whole slide images of haematoxylin and eosin (H&E)-stained biopsies contain valuable information with relation to cancer disease and...

Bioinspired Soft Robot with Incorporated Microelectrodes.

Bioinspired soft robotic systems that mimic living organisms using engineered muscle tissue and biomaterials are revolutionizing the current bioroboti...

Tabu Search and Machine-Learning Classification of Benign and Malignant Proliferative Breast Lesions.

Breast cancer is the most diagnosed cancer among women around the world. The development of computer-aided diagnosis tools is essential to help pathol...

Software-assisted decision support in digital histopathology.

Tissue diagnostics is the world of pathologists, and it is increasingly becoming digitalised to leverage the enormous potential of personalised medici...

Label-Free Leukemia Monitoring by Computer Vision.

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. While there are a number of well-recognized prognostic biomarkers at diagnosis...

A comprehensive study on the multi-class cervical cancer diagnostic prediction on pap smear images using a fusion-based decision from ensemble deep convolutional neural network.

The diagnosis of cervical dysplasia, carcinoma in situ and confirmed carcinoma cases is more easily perceived by commercially available and current re...

Same same but different: A Web-based deep learning application revealed classifying features for the histopathologic distinction of cortical malformations.

OBJECTIVE: The microscopic review of hematoxylin-eosin-stained images of focal cortical dysplasia type IIb and cortical tuber of tuberous sclerosis co...

Machine learning to predict developmental neurotoxicity with high-throughput data from 2D bio-engineered tissues.

There is a growing need for fast and accurate methods for testing developmental neurotoxicity across several chemical exposure sources. Current approa...

Basal and Infectious Enteritis in Broilers Under the I See Inside Methodology: A Chronological Evaluation.

Recently, the inflammation of the intestinal mucosa has been related to many diseases in humans and animals. The concept of Microscopic Enteritis (ME)...

A machine learning model to precisely immunohistochemically classify pituitary adenoma subtypes with radiomics based on preoperative magnetic resonance imaging.

PURPOSE: The type of pituitary adenoma (PA) cannot be clearly recognized with preoperative magnetic resonance imaging (MRI) but can be classified with...

Limited angle tomography for transmission X-ray microscopy using deep learning.

In transmission X-ray microscopy (TXM) systems, the rotation of a scanned sample might be restricted to a limited angular range to avoid collision wit...

Cellular and Molecular Probing of Intact Human Organs.

Optical tissue transparency permits scalable cellular and molecular investigation of complex tissues in 3D. Adult human organs are particularly challe...

Fully automated plaque characterization in intravascular OCT images using hybrid convolutional and lumen morphology features.

For intravascular OCT (IVOCT) images, we developed an automated atherosclerotic plaque characterization method that used a hybrid learning approach, w...