Oncology/Hematology

Optimized generation of high-resolution phantom images using cGAN: Application to quantification of Ki67 breast cancer images.

In pathology, Immunohistochemical staining (IHC) of tissue sections is regularly used to diagnose and grade malignant tumors. Typically, IHC stain interpretation is rendered by a trained pathologist using a manual method, which consists of counting each positively- and negatively-stained cell under a microscope. The manual enumeration suffers from poor reproducibility even in the hands of expert p...

Superpixel-based and boundary-sensitive convolutional neural network for automated liver segmentation.

Segmentation of liver in abdominal computed tomography (CT) is an important step for radiation therapy planning of hepatocellular carcinoma. Practically, a fully automatic segmentation of liver remains challenging because of low soft tissue contrast between liver and its surrounding organs, and its highly deformable shape. The purpose of this work is to develop a novel superpixel-based and boundar...

Development of a Robust Algorithm for Detection of Nuclei and Classification of White Blood Cells in Peripheral Blood Smear Images.

Peripheral Blood Smear analysis plays a vital role in diagnosis of many diseases such as leukemia, anemia, malaria, lymphoma and infections. Unusual v...

Glucagonoma and Glucagonoma Syndrome: One Center's Experience of Six Cases.

Glucagonoma is an extremely rare neuroendocrine tumor arising from pancreatic islet cells. Although patients with glucagonoma manifest multiple typic...

An improved deep learning approach for detection of thyroid papillary cancer in ultrasound images.

Unlike daily routine images, ultrasound images are usually monochrome and low-resolution. In ultrasound images, the cancer regions are usually blurred...

Assessing Breast Cancer Risk with an Artificial Neural Network.

Objectives: Radiologists face uncertainty in making decisions based on their judgment of breast cancer risk. Artificial intelligence and machine learn...

Radiomics - the value of the numbers in present and future radiology.

Radiomics is a new concept that has been functioning in medicine for only a few years. This idea, created recently, relies on processing innumerable q...

An Efficient Mixed-Model for Screening Differentially Expressed Genes of Breast Cancer Based on LR-RF.

To screen differentially expressed genes quickly and efficiently in breast cancer, two gene microarray datasets of breast cancer, GSE15852 and GSE4525...

Gene expression profiles reveal key genes for early diagnosis and treatment of adamantinomatous craniopharyngioma.

Adamantinomatous craniopharyngioma (ACP) is an aggressive brain tumor that occurs predominantly in the pediatric population. Conventional diagnosis me...

Multiple Machine Learning Based-Chemoinformatics Models for Identification of Histone Acetyl Transferase Inhibitors.

The histone acetyl transferase (HAT) are involved in acetylation of histones that lead to transcription activation in numerous gene regulatory mechani...

Unsupervised versus Supervised Identification of Prognostic Factors in Patients with Localized Retroperitoneal Sarcoma: A Data Clustering and Mahalanobis Distance Approach.

The aim of this report is to unveil specific prognostic factors for retroperitoneal sarcoma (RPS) patients by univariate and multivariate statistical ...

Identifying cytokine predictors of cognitive functioning in breast cancer survivors up to 10 years post chemotherapy using machine learning.

INTRODUCTION: The purpose of this study is to explore 13 cytokine predictors of chemotherapy-related cognitive impairment (CRCI) in breast cancer surv...

Texture analysis- and support vector machine-assisted diffusional kurtosis imaging may allow in vivo gliomas grading and IDH-mutation status prediction: a preliminary study.

We sought to investigate, whether texture analysis of diffusional kurtosis imaging (DKI) enhanced by support vector machine (SVM) analysis may provide...

Towards precision informatics of pharmacovigilance: OAE-CTCAE mapping and OAE-based representation and analysis of adverse events in patients treated with cancer drugs.

A critical issue in the usage of cancer drugs is its association with various adverse events (AEs) in some, but not all, patients. The National Cancer...

Stage-Specific Survivability Prediction Models across Different Cancer Types.

For all cancer types, survivability rates vary widely across different stages of cancer. But survivability prediction models built in past were traine...

Identifying tumor in pancreatic neuroendocrine neoplasms from Ki67 images using transfer learning.

The World Health Organization (WHO) has clear guidelines regarding the use of Ki67 index in defining the proliferative rate and assigning grade for pa...

Leaf Extracts and Their Quercetin Protect HepG2 Cell Lines Against CCL Induced Cytotoxicity.

The present study was carried out to evaluate the in vitro cytoprotective effects of and their isolated quercetin fraction to reduce the CCl (carbon ...

A deep Boltzmann machine-driven level set method for heart motion tracking using cine MRI images.

Heart motion tracking for radiation therapy treatment planning can result in effective motion management strategies to minimize radiation-induced card...

Transfer Learning for Molecular Cancer Classification Using Deep Neural Networks.

The emergence of deep learning has impacted numerous machine learning based applications and research. The reason for its success lies in two main adv...

Predicting individual responses to the electroconvulsive therapy with hippocampal subfield volumes in major depression disorder.

Electroconvulsive therapy (ECT) is one of the most effective treatments for major depression disorder (MDD). ECT can induce neurogenesis and synaptoge...