Other Cancers

Nearest Neighbor-Based Strategy to Optimize Multi-View Triplet Network for Classification of Small-Sample Medical Imaging Data.

Multi-view classification with limited sample size and data augmentation is a very common machine learning (ML) problem in medicine. With limited data, a triplet network approach for two-stage representation learning has been proposed. However, effective training and verifying the features from the representation network for their suitability in subsequent classifiers are still unsolved problems. ...

Knowledge Sharing for Pulmonary Nodule Detection in Medical Cyber-Physical Systems.

With the rapid development of edge intelligence (EI) and machine learning (ML), the applications of Cyber-Physical Systems (CPS) have been discovered in all aspects of the life world. As one of its most essential branches, Medical CPS (MCPS) determines human health and medical treatment in the Internet of Everything (IOE) era. Knowledge sharing is the critical point of MCPS and has also been human...

SleepPPG-Net: A Deep Learning Algorithm for Robust Sleep Staging From Continuous Photoplethysmography.

Sleep staging is an essential component in the diagnosis of sleep disorders and management of sleep health. Sleep is traditionally measured in a clini...

Computer-Assisted Diagnosis of Lymph Node Metastases in Colorectal Cancers Using Transfer Learning With an Ensemble Model.

Screening of lymph node metastases in colorectal cancer (CRC) can be a cumbersome task, but it is amenable to artificial intelligence (AI)-assisted di...

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 the extent of the rectal cancer and judge the seve...

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 treatment response for glioblastoma. This assessment is, h...

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 enhancing the diagnosis and improving the breast can...

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 cancer. When cells from the lungs spread to other ti...

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 prevalent form of malignancy among all thyroid cancers ...

Robot-assisted TAMIS: a systematic review of feasibility and outcomes.

BACKGROUND: In the advancement of transanal local excision, robot-assisted transanal minimal invasive surgery is the newest development. In the confin...

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 cancer at diagnosis are generally precluded from curati...

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 segmentation of brain metastases (BMs) in MRI images.

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, it has been a challenge to quantify both 3D tracti...

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. Considering the pathological subtypes, malignancy upgrade r...

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 patients with hypopharyngeal squamous cell carcinoma...

Artificial intelligence in lung cancer diagnosis and prognosis: Current application and future perspective.

Lung cancer is one of the malignant tumors with the highest incidence and mortality in the world. The overall five-year survival rate of lung cancer i...

Machine Learning Models for Predicting the Outcomes of Surgical Treatment of Colorectal Liver Metastases.

BACKGROUND: Surgical intervention remains the cornerstone of a multidisciplinary approach in the treatment of colorectal liver metastases (CLM). Never...

Bioinspired microrobots: Opportunities and challenges in targeted cancer therapy.

Chemotherapy is still the most effective technique to treat many forms of cancer. However, it also carries a high risk of side effects. Numerous nanom...

Radiologists with assistance of deep learning can achieve overall accuracy of benign-malignant differentiation of musculoskeletal tumors comparable with that of pre-surgical biopsies in the literature.

PURPOSE: The purpose of this study was to assess if radiologists assisted by deep learning (DL) algorithms can achieve diagnostic accuracy comparable ...

Robotic omentectomy in gynecologic oncology: surgical anatomy, indications, and a technical approach.

An omentectomy is a standard component care of gynecological cancers, particularly for surgical staging and treatment for malignant ovarian neoplasms,...