Oncology/Hematology

Deep learning automates bidimensional and volumetric tumor burden measurement from MRI in pre- and post-operative glioblastoma patients.

Tumor burden assessment by magnetic resonance imaging (MRI) is central to the evaluation of treatmen...

Dual parallel net: A novel deep learning model for rectal tumor segmentation via CNN and transformer with Gaussian Mixture prior.

Segmentation of rectal cancerous regions from Magnetic Resonance (MR) images can help doctor define ...

MRI brain tumor segmentation using residual Spatial Pyramid Pooling-powered 3D U-Net.

Brain tumor diagnosis has been a lengthy process, and automation of a process such as brain tumor se...

Convolution Neural Network for Breast Cancer Detection and Classification Using Deep Learning.

OBJECTIVE: Early detection and precise diagnosis of breast cancer (BC) plays an essential part in en...

Predicting the Survival of Patients With Cancer From Their Initial Oncology Consultation Document Using Natural Language Processing.

IMPORTANCE: Predicting short- and long-term survival of patients with cancer may improve their care....

Incorporating VR-RENDER Fusion Software in Robot-Assisted Partial Prostatectomy: The First Case Report.

Currently, the active surveillance of men with favorable intermediate-risk localized prostate cancer...

An Unsupervised Learning-Based Regional Deformable Model for Automated Multi-Organ Contour Propagation.

The aim of this study is to evaluate a regional deformable model based on a deep unsupervised learni...

Improved UNet Deep Learning Model for Automatic Detection of Lung Cancer Nodules.

Uncontrolled cell growth in the two spongy lung organs in the chest is the most prevalent kind of ca...

Predicting origin for bone metastatic cancer using deep learning-based pathology.

Annotation-Free Deep Learning-Based Prediction of Thyroid Molecular Cancer Biomarker BRAF (V600E) from Cytological Slides.

Thyroid cancer is the most common endocrine cancer. Papillary thyroid cancer (PTC) is the most preva...

Safety and Feasibility of Robot-Assisted Minimally Invasive Esophagectomy (RAMIE) with Three-Field Lymphadenectomy and Neoadjuvant Chemoradiotherapy in Patients with Resectable Esophageal Cancer and Cervical Lymph Node Metastasis.

BACKGROUND: In the West, patients with cervical lymph node metastasis of resectable esophageal cance...

Utilization of robotics in pediatric surgical oncology.

Despite increasing implementation of robotic surgery and minimally invasive techniques within adult ...

MRI-based two-stage deep learning model for automatic detection and segmentation of brain metastases.

OBJECTIVES: To develop and validate a two-stage deep learning model for automatic detection and segm...

Compatible-domain Transfer Learning for Breast Cancer Classification with Limited Annotated Data.

Microscopic analysis of breast cancer images is the primary task in diagnosing cancer malignancy. Re...

Deep learning based identification of bone scintigraphies containing metastatic bone disease foci.

PURPOSE: Metastatic bone disease (MBD) is the most common form of metastases, most frequently derivi...

Quantifying stiffness and forces of tumor colonies and embryos using a magnetic microrobot.

Stiffness and forces are two fundamental quantities essential to living cells and tissues. However, ...

A deep-learning algorithm to localize basal cell carcinoma foci on Mohs surgery frozen sections.

Prediction of malignancy upgrade rate in high-risk breast lesions using an artificial intelligence model: a retrospective study.

PURPOSE: High-risk breast lesions (HRLs) are associated with future risk of breast cancer. Consideri...

Deep learning-based attenuation map generation with simultaneously reconstructed PET activity and attenuation and low-dose application.

. In PET/CT imaging, CT is used for positron emission tomography (PET) attenuation correction (AC). ...

Deep Learning-Based Dose Prediction for Automated, Individualized Quality Assurance of Head and Neck Radiation Therapy Plans.

PURPOSE: This study aimed to use deep learning-based dose prediction to assess head and neck (HN) pl...

A machine learning model for predicting the three-year survival status of patients with hypopharyngeal squamous cell carcinoma using multiple parameters.

OBJECTIVE: This study aimed to establish a model for predicting the three-year survival status of pa...