Oncology/Hematology

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The field of Medicine and Healthcare has attained revolutionary advancements in the last forty years...

Prediction and interpretation of cancer survival using graph convolution neural networks.

The survival rate of cancer has increased significantly during the past two decades for breast, pros...

Deep learning predicts postsurgical recurrence of hepatocellular carcinoma from digital histopathologic images.

Recurrence risk stratification of patients undergoing primary surgical resection for hepatocellular ...

Sarcoma classification by DNA methylation profiling.

Sarcomas are malignant soft tissue and bone tumours affecting adults, adolescents and children. They...

Deep Learning Regression for Prostate Cancer Detection and Grading in Bi-Parametric MRI.

One of the most common types of cancer in men is prostate cancer (PCa). Biopsies guided by bi-parame...

Artificial immune system features added to breast cancer clinical data for machine learning (ML) applications.

We here propose a new method of combining a mathematical model that describes a chemotherapy treatme...

MethylationToActivity: a deep-learning framework that reveals promoter activity landscapes from DNA methylomes in individual tumors.

Although genome-wide DNA methylomes have demonstrated their clinical value as reliable biomarkers fo...

Editorial Comment to Robot-assisted partial nephrectomy versus standard laparoscopic partial nephrectomy for renal hilar tumor: A prospective multi-institutional study.

On the identification of thyroid nodules using semi-supervised deep learning.

Detecting malign cases from thyroid nodule examinations is crucial in healthcare particularly to imp...

An antibody-free liver cancer screening approach based on nanoplasmonics biosensing chips via spectrum-based deep learning.

The clinical needs of rapidly screening liver cancer in large populations have asked for a facile an...

QAIS-DSNN: Tumor Area Segmentation of MRI Image with Optimized Quantum Matched-Filter Technique and Deep Spiking Neural Network.

Tumor segmentation in brain MRI images is a noted process that can make the tumor easier to diagnose...

A machine learning-based radiomics model for the prediction of axillary lymph-node metastasis in breast cancer.

OBJECTIVE: The aim of this study was to develop and validate machine learning-based radiomics model ...

High performance in risk stratification of intraductal papillary mucinous neoplasms by confocal laser endomicroscopy image analysis with convolutional neural networks (with video).

BACKGROUND AND AIMS: EUS-guided needle-based confocal laser endomicroscopy (EUS-nCLE) can differenti...

A survey of deep learning models in medical therapeutic areas.

Artificial intelligence is a broad field that comprises a wide range of techniques, where deep learn...

Classification of malignant lung cancer using deep learning.

In the automatic detection of suspicious shaded regions on CT images derived from the LIDC-IDRI data...

Intelligent automated drug administration and therapy: future of healthcare.

In the twenty-first century, the collaboration of control engineering and the healthcare sector has ...

Preoperative classification of primary and metastatic liver cancer via machine learning-based ultrasound radiomics.

OBJECTIVE: To investigate the application of machine learning-based ultrasound radiomics in preopera...

Improving prognostic performance in resectable pancreatic ductal adenocarcinoma using radiomics and deep learning features fusion in CT images.

As an analytic pipeline for quantitative imaging feature extraction and analysis, radiomics has grow...

Classification of malignant tumours in breast ultrasound using unsupervised machine learning approaches.

Traditional computer-aided diagnosis (CAD) processes include feature extraction, selection, and clas...

Machine learning predicts lymph node metastasis of poorly differentiated-type intramucosal gastric cancer.

To construct a machine learning algorithm model of lymph node metastasis (LNM) in patients with poor...

Real-time liver tracking algorithm based on LSTM and SVR networks for use in surface-guided radiation therapy.

BACKGROUND: Surface-guided radiation therapy can be used to continuously monitor a patient's surface...