Oncology/Hematology

Automatic Sleep Staging Employing Convolutional Neural Networks and Cortical Connectivity Images.

Understanding of the neuroscientific sleep mechanisms is associated with mental/cognitive and physical well-being and pathological conditions. A prerequisite for further analysis is the identification of the sleep macroarchitecture through manual sleep staging. Several computer-based approaches have been proposed to extract time and/or frequency-domain features with accuracy ranging from 80% to 95...

Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT.

PURPOSE: To develop a deep learning-based computer-aided diagnosis (CAD) system for use in the CT diagnosis of cervical lymph node metastasis (LNM) in patients with thyroid cancer.

Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers.

This study aimed to identify the optimal radiomic machine-learning classifier for differentiating glioblastoma (GBM) from solitary brain metastases (M...

Convolutional neural network for cell classification using microscope images of intracellular actin networks.

Automated cell classification is an important yet a challenging computer vision task with significant benefits to biomedicine. In recent years, there ...

Sleep staging from single-channel EEG with multi-scale feature and contextual information.

PURPOSE: Portable sleep monitoring devices with less-attached sensors and high-accuracy sleep staging methods can expedite sleep disorder diagnosis. T...

A deep neural network improves endoscopic detection of early gastric cancer without blind spots.

BACKGROUND: Gastric cancer is the third most lethal malignancy worldwide. A novel deep convolution neural network (DCNN) to perform visual tasks has b...

Neural Networks for Deep Radiotherapy Dose Analysis and Prediction of Liver SBRT Outcomes.

Stereotactic body radiation therapy (SBRT) is a relatively novel treatment modality, with little post-treatment prognostic information reported. This ...

Dual-energy CT for automatic organs-at-risk segmentation in brain-tumor patients using a multi-atlas and deep-learning approach.

In radiotherapy, computed tomography (CT) datasets are mostly used for radiation treatment planning to achieve a high-conformal tumor coverage while o...

Random forest classifiers aid in the detection of incidental osteoblastic osseous metastases in DEXA studies.

PURPOSE: Dual-energy X-ray absorptiometry (DEXA) studies are used for screening patients for low bone mineral density (BMD). Patients with breast and ...

Reinforcement learning-based control of tumor growth under anti-angiogenic therapy.

BACKGROUND AND OBJECTIVES: In recent decades, cancer has become one of the most fatal and destructive diseases which is threatening humans life. Accor...

Multi-organ segmentation of the head and neck area: an efficient hierarchical neural networks approach.

PURPOSE: In radiation therapy, a key step for a successful cancer treatment is image-based treatment planning. One objective of the planning phase is ...

Classifying Breast Cancer Subtypes Using Multiple Kernel Learning Based on Omics Data.

It is very significant to explore the intrinsic differences in breast cancer subtypes. These intrinsic differences are closely related to clinical dia...

Predicting one-year outcome in first episode psychosis using machine learning.

BACKGROUND: Early illness course correlates with long-term outcome in psychosis. Accurate prediction could allow more focused intervention. Earlier in...

Detection of chromosome structural variation by targeted next-generation sequencing and a deep learning application.

Molecular testing is increasingly important in cancer diagnosis. Targeted next generation sequencing (NGS) is widely accepted method but structural va...

Identification of esophageal cancer pathway deviation and construction of a diagnosis model using three kernel genes.

The purpose of this study is to better understand the role of interleukin 35 (IL35) in esophageal carcinoma by comparing the mRNA level in Barrett's e...

Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks.

Classification of histologic patterns in lung adenocarcinoma is critical for determining tumor grade and treatment for patients. However, this task is...

Natural Language Processing of Radiology Reports in Patients With Hepatocellular Carcinoma to Predict Radiology Resource Utilization.

OBJECTIVE: Radiology is a finite health care resource in high demand at most health centers. However, anticipating fluctuations in demand is a challen...

Automated noncoplanar treatment planning strategy in stereotactic radiosurgery of multiple cranial metastases: HyperArc and CyberKnife dose distributions.

The purpose of this study was to evaluate and compare the dosimetric effects of HyperArc-based stereotactic radiosurgery (SRS) and a robotic radiosurg...

Computer aided quantification of intratumoral stroma yields an independent prognosticator in rectal cancer.

PURPOSE: Tumor-stroma ratio (TSR) serves as an independent prognostic factor in colorectal cancer and other solid malignancies. The recent introductio...

Accurate Patient-Specific Machine Learning Models of Glioblastoma Invasion Using Transfer Learning.

BACKGROUND AND PURPOSE: MR imaging-based modeling of tumor cell density can substantially improve targeted treatment of glioblastoma. Unfortunately, i...