Oncology/Hematology

H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes.

Liver cancer is one of the leading causes of cancer death. To assist doctors in hepatocellular carcinoma diagnosis and treatment planning, an accurate and automatic liver and tumor segmentation method is highly demanded in the clinical practice. Recently, fully convolutional neural networks (FCNs), including 2-D and 3-D FCNs, serve as the backbone in many volumetric image segmentation. However, 2-...

Cancer Drug Response Profile scan (CDRscan): A Deep Learning Model That Predicts Drug Effectiveness from Cancer Genomic Signature.

In the era of precision medicine, cancer therapy can be tailored to an individual patient based on the genomic profile of a tumour. Despite the ever-increasing abundance of cancer genomic data, linking mutation profiles to drug efficacy remains a challenge. Herein, we report Cancer Drug Response profile scan (CDRscan) a novel deep learning model that predicts anticancer drug responsiveness based o...

Is Robotic Complete Mesocolic Excision Feasible for Transverse Colon Cancer?

Laparoscopic complete mesocolic excision (CME) for transverse colon cancer is technically challenging. Robotic technology has been developed to reduc...

Predictive modeling of treatment resistant depression using data from STAR*D and an independent clinical study.

Identification of risk factors of treatment resistance may be useful to guide treatment selection, avoid inefficient trial-and-error, and improve majo...

The Future of Artificial Intelligence in Radiation Oncology.

A Radiomics Approach With CNN for Shear-Wave Elastography Breast Tumor Classification.

This paper proposes a segmentation-free radiomics method to classify malignant and benign breast tumors with shear-wave elastography (SWE) data. The m...

Tracing cell lineages in videos of lens-free microscopy.

In vitro experiments with cultured cells are essential for studying their growth and migration pattern and thus, for gaining a better understanding of...

Modeling asynchronous event sequences with RNNs.

Sequences of events have often been modeled with computational techniques, but typical preprocessing steps and problem settings do not explicitly addr...

MR-Only Brain Radiation Therapy: Dosimetric Evaluation of Synthetic CTs Generated by a Dilated Convolutional Neural Network.

PURPOSE: This work aims to facilitate a fast magnetic resonance (MR)-only workflow for radiation therapy of intracranial tumors. Here, we evaluate whe...

A deep learning approach for real time prostate segmentation in freehand ultrasound guided biopsy.

Targeted prostate biopsy, incorporating multi-parametric magnetic resonance imaging (mp-MRI) and its registration with ultrasound, is currently the st...

Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer.

BACKGROUND: Gene-expression companion diagnostic tests, such as the Oncotype DX test, assess the risk of early stage Estrogen receptor (ER) positive (...

Designing Anticancer Peptides by Constructive Machine Learning.

Constructive (generative) machine learning enables the automated generation of novel chemical structures without the need for explicit molecular desig...

Complex overlapping concepts: An effective auditing methodology for families of similarly structured BioPortal ontologies.

In previous research, we have demonstrated for a number of ontologies that structurally complex concepts (for different definitions of "complex") in a...

Affinity network fusion and semi-supervised learning for cancer patient clustering.

Defining subtypes of complex diseases such as cancer and stratifying patient groups with the same disease but different subtypes for targeted treatmen...

Distinguishing True Progression From Radionecrosis After Stereotactic Radiation Therapy for Brain Metastases With Machine Learning and Radiomics.

PURPOSE: Treatment effect or radiation necrosis after stereotactic radiosurgery (SRS) for brain metastases is a common phenomenon often indistinguisha...

Novel Breast Imaging and Machine Learning: Predicting Breast Lesion Malignancy at Cone-Beam CT Using Machine Learning Techniques.

OBJECTIVE: The purpose of this study is to evaluate the diagnostic performance of machine learning techniques for malignancy prediction at breast cone...

A DNA Nanorobot Uprises against Cancer.

DNA-based nanorobots have been shown to sense and respond to molecular triggers, such as intracellular pH and cell surface receptors. A recent report ...

Optimal two-stage dynamic treatment regimes from a classification perspective with censored survival data.

Clinicians often make multiple treatment decisions at key points over the course of a patient's disease. A dynamic treatment regime is a sequence of d...

Machine Learning-based Texture Analysis of Contrast-enhanced MR Imaging to Differentiate between Glioblastoma and Primary Central Nervous System Lymphoma.

PURPOSE: Although advanced MRI techniques are increasingly available, imaging differentiation between glioblastoma and primary central nervous system ...

A Deep Learning Approach for Targeted Contrast-Enhanced Ultrasound Based Prostate Cancer Detection.

The important role of angiogenesis in cancer development has driven many researchers to investigate the prospects of noninvasive cancer diagnosis base...