AIMC Topic: Neoplasms

Clear Filters Showing 1181 to 1190 of 2356 articles

Quantitative PET in the 2020s: a roadmap.

Physics in medicine and biology
Positron emission tomography (PET) plays an increasingly important role in research and clinical applications, catalysed by remarkable technical advances and a growing appreciation of the need for reliable, sensitive biomarkers of human function in h...

Diagnosis prediction of tumours of unknown origin using ImmunoGenius, a machine learning-based expert system for immunohistochemistry profile interpretation.

Diagnostic pathology
BACKGROUND: Immunohistochemistry (IHC) remains the gold standard for the diagnosis of pathological diseases. This technique has been supporting pathologists in making precise decisions regarding differential diagnosis and subtyping, and in creating p...

Fine-Tuning and training of densenet for histopathology image representation using TCGA diagnostic slides.

Medical image analysis
Feature vectors provided by pre-trained deep artificial neural networks have become a dominant source for image representation in recent literature. Their contribution to the performance of image analysis can be improved through fine-tuning. As an ul...

Gated Graph Attention Network for Cancer Prediction.

Sensors (Basel, Switzerland)
With its increasing incidence, cancer has become one of the main causes of worldwide mortality. In this work, we mainly propose a novel attention-based neural network model named Gated Graph ATtention network (GGAT) for cancer prediction, where a gat...

Use of deep learning for detection, characterisation and prediction of metastatic disease from computerised tomography: a systematic review.

Postgraduate medical journal
CT is widely used for diagnosis, staging and management of cancer. The presence of metastasis has significant implications on treatment and prognosis. Deep learning (DL), a form of machine learning, where layers of programmed algorithms interpret and...

Deep active learning for classifying cancer pathology reports.

BMC bioinformatics
BACKGROUND: Automated text classification has many important applications in the clinical setting; however, obtaining labelled data for training machine learning and deep learning models is often difficult and expensive. Active learning techniques ma...

The prognostic impact of the tumour stroma fraction: A machine learning-based analysis in 16 human solid tumour types.

EBioMedicine
BACKGROUND: The development of a reactive tumour stroma is a hallmark of tumour progression and pronounced tumour stroma is generally considered to be associated with clinical aggressiveness. The variability between tumour types regarding stroma frac...

DeepVISP: Deep Learning for Virus Site Integration Prediction and Motif Discovery.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Approximately 15% of human cancers are estimated to be attributed to viruses. Virus sequences can be integrated into the host genome, leading to genomic instability and carcinogenesis. Here, a new deep convolutional neural network (CNN) model is deve...

Artificial Intelligence Techniques That May Be Applied to Primary Care Data to Facilitate Earlier Diagnosis of Cancer: Systematic Review.

Journal of medical Internet research
BACKGROUND: More than 17 million people worldwide, including 360,000 people in the United Kingdom, were diagnosed with cancer in 2018. Cancer prognosis and disease burden are highly dependent on the disease stage at diagnosis. Most people diagnosed w...

A machine learning-based gene signature of response to the novel alkylating agent LP-184 distinguishes its potential tumor indications.

BMC bioinformatics
BACKGROUND: Non-targeted cytotoxics with anticancer activity are often developed through preclinical stages using response criteria observed in cell lines and xenografts. A panel of the NCI-60 cell lines is frequently the first line to define tumor t...