AI Medical Compendium Topic:
Neoplasms

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EpICC: A Bayesian neural network model with uncertainty correction for a more accurate classification of cancer.

Scientific reports
Accurate classification of cancers into their types and subtypes holds the key for choosing the right treatment strategy and can greatly impact patient well-being. However, existence of large-scale variations in the molecular processes driving even a...

A framework for falsifiable explanations of machine learning models with an application in computational pathology.

Medical image analysis
In recent years, deep learning has been the key driver of breakthrough developments in computational pathology and other image based approaches that support medical diagnosis and treatment. The underlying neural networks as inherent black boxes lack ...

A survey on gene expression data analysis using deep learning methods for cancer diagnosis.

Progress in biophysics and molecular biology
Gene Expression Data is the biological data to extract meaningful hidden information from the gene dataset. This gene information is used for disease diagnosis especially in cancer treatment based on the variations in gene expression levels. DNA micr...

Trends in the surgical management of parapharyngeal space tumors: A single-center retrospective analysis.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
PURPOSE: Surgery remains the mainstay treatment for parapharyngeal space (PPS) tumors. Given the rapid advance and increasing usage of endoscopic and robotic techniques, we aimed to investigate the surgical trends of PPS tumors in our institution and...

An artificial intelligence model predicts the survival of solid tumour patients from imaging and clinical data.

European journal of cancer (Oxford, England : 1990)
BACKGROUND: The need for developing new biomarkers is increasing with the emergence of many targeted therapies. Artificial Intelligence (AI) algorithms have shown great promise in the medical imaging field to build predictive models. We developed a p...

Biomarker identification by reversing the learning mechanism of an autoencoder and recursive feature elimination.

Molecular omics
RNA-Seq has made significant contributions to various fields, particularly in cancer research. Recent studies on differential gene expression analysis and the discovery of novel cancer biomarkers have extensively used RNA-Seq data. New biomarker iden...

Using Sparse Patch Annotation for Tumor Segmentation in Histopathological Images.

Sensors (Basel, Switzerland)
Tumor segmentation is a fundamental task in histopathological image analysis. Creating accurate pixel-wise annotations for such segmentation tasks in a fully-supervised training framework requires significant effort. To reduce the burden of manual an...

Anti-Colorectal Cancer Effects of (Bull.: Fr.) P. Karst. Spore Powder through Regulation of Gut Microbiota-Mediated JAK/STAT Signaling.

Nutrients
(Bull.: Fr.) P. Karst. spore powder (IHS) contains polyphenols and triterpenoids with pharmacological effects. Here, we analyzed its composition, and we investigated the effects of IHS on colorectal cancer (CRC) in B6/JGpt-/Gpt () mice and its poten...

Towards Precision Oncology: Enhancing Cancer Screening, Diagnosis and Theragnosis Using Artificial Intelligence.

Current oncology (Toronto, Ont.)
Highly complex and multi-dimensional medical data containing clinical, radiologic, pathologic, and sociodemographic information have the potential to advance precision oncology [...].

Automated Recognition of Cancer Tissues through Deep Learning Framework from the Photoacoustic Specimen.

Contrast media & molecular imaging
The fast advancement of biomedical research technology has expanded and enhanced the spectrum of diagnostic instruments. Various research groups have found optical imaging, ultrasonic imaging, and magnetic resonance imaging to create multifunctional ...