Cardiovascular

A Hybridized ELM for Automatic Micro Calcification Detection in Mammogram Images Based on Multi-Scale Features.

Detection of masses and micro calcifications are a stimulating task for radiologists in digital mammogram images. Radiologists using Computer Aided Detection (CAD) frameworks to find the breast lesion. Micro calcification may be the early sign of breast cancer. There are different kinds of methods used to detect and recognize micro calcification from mammogram images. This paper presents an ELM (E...

Machine learning for prediction of chemoradiation therapy response in rectal cancer using pre-treatment and mid-radiation multi-parametric MRI.

PURPOSE: To predict the neoadjuvant chemoradiation therapy (CRT) response in patients with locally advanced rectal cancer (LARC) using radiomics and deep learning based on pre-treatment MRI and a mid-radiation follow-up MRI taken 3-4 weeks after the start of CRT.

A Novel Internet of Things Framework Integrated with Real Time Monitoring for Intelligent Healthcare Environment.

During mammogram screening, there is a higher probability that detection of cancers is missed, and more than 16 percentage of breast cancer is not det...

Machine-learned target volume delineation of F-FDG PET images after one cycle of induction chemotherapy.

Biological tumour volume (GTV) delineation on F-FDG PET acquired during induction chemotherapy (ICT) is challenging due to the reduced metabolic uptak...

Deep Residual Inception Encoder-Decoder Network for Medical Imaging Synthesis.

Image synthesis is a novel solution in precision medicine for scenarios where important medical imaging is not otherwise available. The convolutional ...

Attention-aware fully convolutional neural network with convolutional long short-term memory network for ultrasound-based motion tracking.

PURPOSE: One of the promising options for motion management in radiation therapy (RT) is the use of LINAC-compatible robotic-arm-mounted ultrasound im...

Derivation of an optimal trajectory and nonlinear adaptive controller design for drug delivery in cancerous tumor chemotherapy.

Numerous models have investigated cancer behavior by considering different factors in chemotherapy. The subject of a controller design approach for th...

Viable and necrotic tumor assessment from whole slide images of osteosarcoma using machine-learning and deep-learning models.

Pathological estimation of tumor necrosis after chemotherapy is essential for patients with osteosarcoma. This study reports the first fully automated...

Machine Learning to Build and Validate a Model for Radiation Pneumonitis Prediction in Patients with Non-Small Cell Lung Cancer.

PURPOSE: Radiation pneumonitis is an important adverse event in patients with non-small cell lung cancer (NSCLC) receiving thoracic radiotherapy. Howe...

Prognostic Value of Deep Learning PET/CT-Based Radiomics: Potential Role for Future Individual Induction Chemotherapy in Advanced Nasopharyngeal Carcinoma.

PURPOSE: We aimed to evaluate the value of deep learning on positron emission tomography with computed tomography (PET/CT)-based radiomics for individ...

Combining handcrafted features with latent variables in machine learning for prediction of radiation-induced lung damage.

PURPOSE: There has been burgeoning interest in applying machine learning methods for predicting radiotherapy outcomes. However, the imbalanced ratio o...

Dosimetric study on learning-based cone-beam CT correction in adaptive radiation therapy.

INTRODUCTION: Cone-beam CT (CBCT) image quality is important for its quantitative analysis in adaptive radiation therapy. However, due to severe artif...

Deep Learning for Automated Contouring of Primary Tumor Volumes by MRI for Nasopharyngeal Carcinoma.

Background Nasopharyngeal carcinoma (NPC) may be cured with radiation therapy. Tumor proximity to critical structures demands accuracy in tumor deline...

A Remotely Controlled Transformable Soft Robot Based on Engineered Cardiac Tissue Construct.

Many living organisms undergo conspicuous or abrupt changes in body structure, which is often accompanied by a behavioral change. Inspired by the natu...

Learning Where to See: A Novel Attention Model for Automated Immunohistochemical Scoring.

Estimating over-amplification of human epidermal growth factor receptor 2 (HER2) on invasive breast cancer is regarded as a significant predictive and...

A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning.

In this paper we present a method for simultaneously segmenting brain tumors and an extensive set of organs-at-risk for radiation therapy planning of ...

CT male pelvic organ segmentation using fully convolutional networks with boundary sensitive representation.

Accurate segmentation of the prostate and organs at risk (e.g., bladder and rectum) in CT images is a crucial step for radiation therapy in the treatm...

Combination of Peri- and Intratumoral Radiomic Features on Baseline CT Scans Predicts Response to Chemotherapy in Lung Adenocarcinoma.

PURPOSE: To identify the role of radiomics texture features both within and outside the nodule in predicting time to progression (TTP) and overall su...

3D radiotherapy dose prediction on head and neck cancer patients with a hierarchically densely connected U-net deep learning architecture.

The treatment planning process for patients with head and neck (H&N) cancer is regarded as one of the most complicated due to large target volume, mul...

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 ...