Oncology/Hematology

Efficient automated detection of mitotic cells from breast histological images using deep convolution neutral network with wavelet decomposed patches.

In medical practice, the mitotic cell count from histological images acts as a proliferative marker for cancer diagnosis. Therefore, an accurate method for detecting mitotic cells in histological images is essential for cancer screening. Manual evaluation of clinically relevant image features that might reflect mitotic cells in histological images is time-consuming and error prone, due to the hete...

Predicting the naturalistic course of depression from a wide range of clinical, psychological, and biological data: a machine learning approach.

Many variables have been linked to different course trajectories of depression. These findings, however, are based on group comparisons with unknown translational value. This study evaluated the prognostic value of a wide range of clinical, psychological, and biological characteristics for predicting the course of depression and aimed to identify the best set of predictors. Eight hundred four unip...

Mixture Model Segmentation System for Parasagittal Meningioma brain Tumor Classification based on Hybrid Feature Vector.

Meningioma is the one of the most common type of brain tumor, it as arises from the meninges and encloses the spine and the brain inside the skull. It...

A graph-based lesion characterization and deep embedding approach for improved computer-aided diagnosis of nonmass breast MRI lesions.

Nonmass-like enhancements are a common but diagnostically challenging finding in breast MRI. Nonmass-like lesions can be described as clusters of spat...

LogLoss-BERAF: An ensemble-based machine learning model for constructing highly accurate diagnostic sets of methylation sites accounting for heterogeneity in prostate cancer.

Although modern methods of whole genome DNA methylation analysis have a wide range of applications, they are not suitable for clinical diagnostics due...

Large-Scale Multi-Class Image-Based Cell Classification With Deep Learning.

Recent advances in ultra-high-throughput microscopy have enabled a new generation of cell classification methodologies using image-based cell phenotyp...

Patch-based classification of thyroid nodules in ultrasound images using direction independent features extracted by two-threshold binary decomposition.

Ultrasound imaging of the thyroid gland is considered to be the best diagnostic choice for evaluating thyroid nodules in early stages, since it has be...

The application of artificial intelligence in the IMRT planning process for head and neck cancer.

Artificial intelligence (AI) is beginning to transform IMRT treatment planning for head and neck patients. However, the complexity and novelty of AI a...

Convolutional Neural Networks Promising in Lung Cancer T-Parameter Assessment on Baseline FDG-PET/CT.

AIM: To develop an algorithm, based on convolutional neural network (CNN), for the classification of lung cancer lesions as T1-T2 or T3-T4 on staging ...

Orthotopic Liver Transplantation After Stereotactic Body Radiotherapy for Pediatric Hepatocellular Carcinoma with Central Biliary Obstruction and Nodal Involvement.

Here we describe the case of a 10-year-old boy with a history of chronic hepatitis B who was diagnosed with hepatocellular carcinoma (HCC) with a larg...

Results of Phase 1 study on cytoreductive radical prostatectomy in men with newly diagnosed metastatic prostate cancer.

BACKGROUND: Preclinical and retrospective data suggest that cytoreductive radical prostatectomy may benefit a subset of men who present with metastati...

Fully Automated Delineation of Gross Tumor Volume for Head and Neck Cancer on PET-CT Using Deep Learning: A Dual-Center Study.

PURPOSE: In this study, we proposed an automated deep learning (DL) method for head and neck cancer (HNC) gross tumor volume (GTV) contouring on posit...

Classification of acute lymphoblastic leukemia using deep learning.

Acute Leukemia is a life-threatening disease common both in children and adults that can lead to death if left untreated. Acute Lymphoblastic Leukemia...

Iterative quality enhancement via residual-artifact learning networks for low-dose CT.

Radiation exposure and the associated risk of cancer for patients in computed tomography (CT) scans have been major clinical concerns. The radiation e...

Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification.

Correctly identifying sleep stages is important in diagnosing and treating sleep disorders. This paper proposes a joint classification-and-prediction ...

Sparse Representation Over Learned Dictionaries on the Riemannian Manifold for Automated Grading of Nuclear Pleomorphism in Breast Cancer.

Breast cancer is found to be the most pervasive type of cancer among women. Computer aided detection and diagnosis of cancer at the initial stages can...

Clinically validated machine learning algorithm for detecting residual diseases with multicolor flow cytometry analysis in acute myeloid leukemia and myelodysplastic syndrome.

BACKGROUND: Multicolor flow cytometry (MFC) analysis is widely used to identify minimal residual disease (MRD) after treatment for acute myeloid leuke...

A methodological approach for deep learning to distinguish between meningiomas and gliomas on canine MR-images.

BACKGROUND: Distinguishing between meningeal-based and intra-axial lesions by means of magnetic resonance (MR) imaging findings may occasionally be ch...

Predicting Advanced Prostate Cancer from Modeling Early Indications in Biopsy and Prostatectomy Samples via Transductive Semi-Supervised Survival Analysis.

Prostate cancer is the most prevalent form of cancer and the second most common cause of cancer deaths among men in the United States. Accurate progno...

Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing.

Automatic liver tumor segmentation would have a big impact on liver therapy planning procedures and follow-up assessment, thanks to standardization an...