Oncology/Hematology

Personalized Breast Cancer Treatments Using Artificial Intelligence in Radiomics and Pathomics.

Progress in computing power and advances in medical imaging over recent decades have culminated in new opportunities for artificial intelligence (AI), computer vision, and using radiomics to facilitate clinical decision-making. These opportunities are growing in medical specialties, such as radiology, pathology, and oncology. As medical imaging and pathology are becoming increasingly digitized, it...

A Cancer Survival Prediction Method Based on Graph Convolutional Network.

BACKGROUND AND OBJECTIVE: Cancer, as the most challenging part in the human disease history, has always been one of the main threats to human life and health. The high mortality of cancer is largely due to the complexity of cancer and the significant differences in clinical outcomes. Therefore, it will be significant to improve accuracy of cancer survival prediction, which has become one of the ma...

Real-time automated diagnosis of precancerous lesions and early esophageal squamous cell carcinoma using a deep learning model (with videos).

BACKGROUND AND AIMS: We developed a system for computer-assisted diagnosis (CAD) for real-time automated diagnosis of precancerous lesions and early e...

A Human Microrobot Interface Based on Acoustic Manipulation.

Micro/nanorobotic systems capable of targeted transporting and releasing hold considerable promise for drug delivery, cellular surgery, biosensing, na...

An investigation of quantitative accuracy for deep learning based denoising in oncological PET.

Reducing radiation dose is important for PET imaging. However, reducing injection doses causes increased image noise and low signal-to-noise ratio (SN...

Predicting real-time 3D deformation field maps (DFM) based on volumetric cine MRI (VC-MRI) and artificial neural networks for on-board 4D target tracking: a feasibility study.

To predict real-time 3D deformation field maps (DFMs) using Volumetric Cine MRI (VC-MRI) and adaptive boosting and multi-layer perceptron neural netwo...

Deeply-Supervised Networks With Threshold Loss for Cancer Detection in Automated Breast Ultrasound.

ABUS, or Automated breast ultrasound, is an innovative and promising method of screening for breast examination. Comparing to common B-mode 2D ultraso...

Precision Surgical Therapy for Adenocarcinoma of the Esophagus and Esophagogastric Junction.

INTRODUCTION: To facilitate the initial clinical decision regarding whether to use esophagectomy alone or neoadjuvant therapy in surgical care for ind...

Convolution kernel and iterative reconstruction affect the diagnostic performance of radiomics and deep learning in lung adenocarcinoma pathological subtypes.

BACKGROUND: The aim of this study was to investigate the influence of convolution kernel and iterative reconstruction on the diagnostic performance of...

Multigene signatures of responses to chemotherapy derived by biochemically-inspired machine learning.

Pharmacogenomic responses to chemotherapy drugs can be modeled by supervised machine learning of expression and copy number of relevant gene combinati...

Machine learning application for prediction of locoregional recurrences in early oral tongue cancer: a Web-based prognostic tool.

Estimation of risk of recurrence in early-stage oral tongue squamous cell carcinoma (OTSCC) remains a challenge in the field of head and neck oncology...

An improved fuzzy-differential evolution approach applied to classification of tumors in liver CT scan images.

Fuzzy inference systems have been frequently used in medical diagnosis for managing uncertainty sources in the medical images. In addition, fuzzy syst...

Documentation of Palliative and End-of-Life Care Process Measures Among Young Adults Who Died of Cancer: A Natural Language Processing Approach.

Few studies have investigated palliative and end-of-life care processes among young adults (YAs), aged 18-34 years, who died of cancer. This retrospec...

Fully automatic catheter segmentation in MRI with 3D convolutional neural networks: application to MRI-guided gynecologic brachytherapy.

External-beam radiotherapy followed by high dose rate (HDR) brachytherapy is the standard-of-care for treating gynecologic cancers. The enhanced soft-...

Multi-Task Deep Model With Margin Ranking Loss for Lung Nodule Analysis.

Lung cancer is the leading cause of cancer deaths worldwide and early diagnosis of lung nodule is of great importance for therapeutic treatment and sa...

Classification and Segmentation of Hyperspectral Data of Hepatocellular Carcinoma Samples Using 1-D Convolutional Neural Network.

Pathological diagnosis plays an important role in the diagnosis and treatment of hepatocellular carcinoma (HCC). The traditional method of pathologica...

An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis.

The microscopic assessment of tissue samples is instrumental for the diagnosis and staging of cancer, and thus guides therapy. However, these assessme...

Machine learning defined diagnostic criteria for differentiating pituitary metastasis from autoimmune hypophysitis in patients undergoing immune checkpoint blockade therapy.

PURPOSE: New-onset pituitary gland lesions are observed in up to 18% of cancer patients undergoing treatment with immune checkpoint blockers (ICB). We...

Challenges Facing the Detection of Colonic Polyps: What Can Deep Learning Do?

Colorectal cancer (CRC) is one of the most common causes of cancer mortality in the world. The incidence is related to increases with age and western ...

An investigation of CNN models for differentiating malignant from benign lesions using small pathologically proven datasets.

Cancer has been one of the most threatening diseases to human health. There have been many efforts devoted to the advancement of radiology and transfo...