Oncology/Hematology

Collaborative, Multidisciplinary Evaluation of Cancer Variants Through Virtual Molecular Tumor Boards Informs Local Clinical Practices.

PURPOSE: The cancer research community is constantly evolving to better understand tumor biology, disease etiology, risk stratification, and pathways to novel treatments. Yet the clinical cancer genomics field has been hindered by redundant efforts to meaningfully collect and interpret disparate data types from multiple high-throughput modalities and integrate into clinical care processes. Bespoke...

A Brain Tumor Segmentation Framework Based on Outlier Detection Using One-Class Support Vector Machine.

Accurate segmentation of brain tumors is a challenging task and also a crucial step in diagnosis and treatment planning for cancer patients. Magnetic resonance imaging (MRI) is the standard imaging modality for detection, characterization, treatment planning and outcome evaluation of brain tumors. MRI scans are usually acquired at multiple sessions before and after the treatment. An automatic segm...

Computer-Aided Diagonosis for Colorectal Cancer using Deep Learning with Visual Explanations.

Detection, diagnosis, and removal of colorectal neoplasms are well-accepted colorectal cancer prevention methods. Although promising endoscopic imagin...

Carcino-Net: A Deep Learning Framework for Automated Gleason Grading of Prostate Biopsies.

Gleason scoring for prostate cancer grading is a subjective examination and suffers from suboptimal interobserver and intraobserver variability. To ov...

Interphase Cell Cycle Staging using Deep Learning.

The progression of cells through the cell cycle is a tightly regulated process and is known to be key in maintaining normal tissue architecture and fu...

Deep learning approaches for bone and bone lesion segmentation on 18FDG PET/CT imaging in the context of metastatic breast cancer.

FDG PET/CT imaging is commonly used in diagnosis and follow-up of metastatic breast cancer, but its quantitative analysis is complicated by the number...

Combining Superpixels and Deep Learning Approaches to Segment Active Organs in Metastatic Breast Cancer PET Images.

Semi-automatic measurements are performed on FDG PET-CT images to monitor the evolution of metastatic sites in the clinical follow-up of metastatic br...

Training Deep Neural Networks for Small and Highly Heterogeneous MRI Datasets for Cancer Grading.

Using medical images recorded in clinical practice has the potential to be a game-changer in the application of machine learning for medical decision ...

Artificial Neural Network enabling Clinically Meaningful Biological Image Data Generation.

Biological experiments for developing efficient cancer therapeutics require significant resources of time and costs particularly in acquiring biologic...

A Composite Material Based Neural Network for Tissue Mechanical Properties Estimation Toward Stage Assessment of Infarction.

Cardiac biomechanical modelling is a promising new tool to be used in prognostic medicine and therapy planning for patients suffering from a variety o...

Palpation-Based Multi-Tumor Detection Method Considering Moving Distance for Robot-assisted Minimally Invasive Surgery.

A novel palpation-based tumor detection method for robot-assisted minimally invasive surgery (RMIS) is proposed in this paper. A tactile stiffness sen...

Evaluating the Predictability of Cancer Types from 536 Somatic Mutations: A New Dataset.

In this paper, we introduce a new dataset for cancer research containing somatic mutation states of 536 genes of the Cancer Gene Census (CGC). We used...

Predicting the pathogenicity of protein coding mutations using Natural Language Processing.

DNA-Sequencing of tumor cells has revealed thousands of genetic mutations. However, cancer is caused by only some of them. Identifying mutations that ...

Intelligent image-activated cell sorting 2.0.

The advent of intelligent image-activated cell sorting (iIACS) has enabled high-throughput intelligent image-based sorting of single live cells from h...

[Application of convolutional neural network to risk evaluation of positive circumferential resection margin of rectal cancer by magnetic resonance imaging].

To explore the feasibility of using faster regional convolutional neural network (Faster R-CNN) to evaluate the status of circumferential resection m...

Elucidating the interaction dynamics between microswimmer body and immune system for medical microrobots.

The structural design parameters of a medical microrobot, such as the morphology and surface chemistry, should aim to minimize any physical interactio...

Comparison of Unplanned 30-Day Readmission Prediction Models, Based on Hospital Warehouse and Demographic Data.

Anticipating unplanned hospital readmission episodes is a safety and medico-economic issue. We compared statistics (Logistic Regression) and machine l...

Advanced Machine Learning in Prediction of Second Primary Cancer in Colorectal Cancer.

Colorectal cancer (CRC) ranked third among most commonly diagnosed cancers worldwide. The onset of second primary cancer (SPC) is an important indicat...

Classification of Histologic Images Using a Single Staining: Experiments with Deep Learning on Deconvolved Images.

The automated analysis of digitized immunohistochemistry microscope slides is usually a challenging task, because markers should be analysed on the tu...

Evaluation of transfer learning of pre-trained CNNs applied to breast cancer detection on infrared images.

Breast cancer accounts for the highest number of female deaths worldwide. Early detection of the disease is essential to increase the chances of treat...