Oncology/Hematology

Enzyme-powered Janus platelet cell robots for active and targeted drug delivery.

Transforming natural cells into functional biocompatible robots capable of active movement is expected to enhance the functions of the cells and revolutionize the development of synthetic micromotors. However, present cell-based micromotor systems commonly require the propulsion capabilities of rigid motors, external fields, or harsh conditions, which may compromise biocompatibility and require co...

Conventional Machine Learning and Deep Learning Approach for Multi-Classification of Breast Cancer Histopathology Images-a Comparative Insight.

Automatic multi-classification of breast cancer histopathological images has remained one of the top-priority research areas in the field of biomedical informatics, due to the great clinical significance of multi-classification in providing diagnosis and prognosis of breast cancer. In this work, two machine learning approaches are thoroughly explored and compared for the task of automatic magnific...

Concordance Study in Hepatectomy Recommendations Between Watson for Oncology and Clinical Practice for Patients with Hepatocellular Carcinoma in China.

BACKGROUND: With the improvement in diagnostic imaging, perioperative care and surgical technique, the indications and complexity of liver resections ...

eBreCaP: extreme learning-based model for breast cancer survival prediction.

Breast cancer is the second leading cause of death in the world. Breast cancer research is focused towards its early prediction, diagnosis, and progno...

Genomics models in radiotherapy: From mechanistic to machine learning.

Machine learning (ML) provides a broad framework for addressing high-dimensional prediction problems in classification and regression. While ML is oft...

Machine and deep learning methods for radiomics.

Radiomics is an emerging area in quantitative image analysis that aims to relate large-scale extracted imaging information to clinical and biological ...

Machine learning for radiation outcome modeling and prediction.

AIMS: This review paper intends to summarize the application of machine learning to radiotherapy outcome modeling based on structured and un-structure...

Introduction to machine and deep learning for medical physicists.

Recent years have witnessed tremendous growth in the application of machine learning (ML) and deep learning (DL) techniques in medical physics. Embrac...

Thyroid Nodule Malignancy Risk Stratification Using a Convolutional Neural Network.

This study evaluates the performance of convolutional neural networks (CNNs) in risk stratifying the malignant potential of thyroid nodules alongside ...

A Fully Automated Deep Learning Network for Brain Tumor Segmentation.

We developed a fully automated method for brain tumor segmentation using deep learning; 285 brain tumor cases with multiparametric magnetic resonance ...

Multifunctional surface microrollers for targeted cargo delivery in physiological blood flow.

Mobile microrobots offer great promise for minimally invasive targeted medical theranostic applications at hard-to-access regions inside the human bod...

Comparative outcomes of robot-assisted minimally invasive versus open esophagectomy in patients with esophageal squamous cell carcinoma: a propensity score-weighted analysis.

Robots are increasingly used in minimally invasive surgery. We evaluated the clinical benefits of robot-assisted minimally invasive esophagectomy (RAM...

Artificial Intelligence Will Not Replace Health Professionals, but the Proper Use of Artificial Intelligence Will Make Health Professionals Better.

Deep learning has enabled great advances to be made in cancer research with regards to diagnosis, prognosis, and treatment. The study by Wang and coll...

Expert knowledge for the recognition of leukemic cells.

This work shows the advantage of expert knowledge for leukemic cell recognition. In the medical area, visual analysis of microscopic images has regula...

Improving Augmented Human Intelligence to Distinguish Burkitt Lymphoma From Diffuse Large B-Cell Lymphoma Cases.

OBJECTIVES: To assess and improve the assistive role of a deep, densely connected convolutional neural network (CNN) to hematopathologists in differen...

The study of multiple diagnosis models of human prostate cancer based on Taylor database by artificial neural networks.

BACKGROUND: Prostate cancer (PCa) is the most common malignancy seen in men and the second leading cause of cancer-related death in males. The inciden...

Machine Learning and Deep Neural Networks: Applications in Patient and Scan Preparation, Contrast Medium, and Radiation Dose Optimization.

Artificial intelligence (AI) algorithms are dependent on a high amount of robust data and the application of appropriate computational power and softw...

Classifying non-small cell lung cancer types and transcriptomic subtypes using convolutional neural networks.

OBJECTIVE: Non-small cell lung cancer is a leading cause of cancer death worldwide, and histopathological evaluation plays the primary role in its dia...

Efficient and Accurate Extracting of Unstructured EHRs on Cancer Therapy Responses for the Development of RECIST Natural Language Processing Tools: Part I, the Corpus.

PURPOSE: Electronic health records (EHRs) are created primarily for nonresearch purposes; thus, the amounts of data are enormous, and the data are cru...

[Research status and trend of artificial intelligence in the diagnosis of urinary diseases].

Recently, artificial intelligence (AI) has been widely applied in the diagnosis and treatment of urinary diseases with the development of data storage...