Oncology/Hematology

Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology.

In the past decade, advances in precision oncology have resulted in an increased demand for predictive assays that enable the selection and stratification of patients for treatment. The enormous divergence of signalling and transcriptional networks mediating the crosstalk between cancer, stromal and immune cells complicates the development of functionally relevant biomarkers based on a single gene...

Network as a Biomarker: A Novel Network-Based Sparse Bayesian Machine for Pathway-Driven Drug Response Prediction.

With the advances in different biological networks including gene regulation, gene co-expression, protein-protein interaction networks, and advanced approaches for network reconstruction, analysis, and interpretation, it is possible to discover reliable and accurate molecular network-based biomarkers for monitoring cancer treatment. Such efforts will also pave the way toward the realization of bio...

Cytokeratin-Supervised Deep Learning for Automatic Recognition of Epithelial Cells in Breast Cancers Stained for ER, PR, and Ki-67.

Immunohistochemistry (IHC) of ER, PR, and Ki-67 are routinely used assays in breast cancer diagnostics. Determination of the proportion of stained cel...

Synthetic CT reconstruction using a deep spatial pyramid convolutional framework for MR-only breast radiotherapy.

PURPOSE: The superior soft-tissue contrast achieved using magnetic resonance imaging (MRI) compared to x-ray computed tomography (CT) has led to the p...

Machine learning applications in prostate cancer magnetic resonance imaging.

With this review, we aimed to provide a synopsis of recently proposed applications of machine learning (ML) in radiology focusing on prostate magnetic...

A Deep Learning Model to Triage Screening Mammograms: A Simulation Study.

Background Recent deep learning (DL) approaches have shown promise in improving sensitivity but have not addressed limitations in radiologist specific...

Gene Ontology and Expression Studies of Strigolactone Analogues on a Hepatocellular Carcinoma Cell Line.

Human hepatocellular carcinoma (HCC) is the most common and recurrent type of primary adult liver cancer without any effective therapy. Plant-derived ...

Automated age estimation from MRI volumes of the hand.

Highly relevant for both clinical and legal medicine applications, the established radiological methods for estimating unknown age in children and ado...

3D segmentation of nasopharyngeal carcinoma from CT images using cascade deep learning.

In the paper, we propose a new deep learning-based method for segmenting nasopharyngeal carcinoma (NPC) in the nasopharynx from three orthogonal CT im...

Support Vector Machine Classification of Nonmelanoma Skin Lesions Based on Fluorescence Lifetime Imaging Microscopy.

Early diagnosis of malignant skin lesions is critical for prompt treatment and a clinical prognosis of skin cancers. However, it is difficult to preci...

Inferring Gene Regulatory Networks of Metabolic Enzymes Using Gradient Boosted Trees.

Metabolic reprogramming is a hallmark of cancer. In cancer cells, transcription factors (TFs) govern metabolic reprogramming through abnormally increa...

Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network.

BACKGROUND: Understanding the phenotypic drug response on cancer cell lines plays a vital role in anti-cancer drug discovery and re-purposing. The Gen...

A whole slide image-based machine learning approach to predict ductal carcinoma in situ (DCIS) recurrence risk.

BACKGROUND: Breast ductal carcinoma in situ (DCIS) represent approximately 20% of screen-detected breast cancers. The overall risk for DCIS patients t...

Automated detection and classification of early AMD biomarkers using deep learning.

Age-related macular degeneration (AMD) affects millions of people and is a leading cause of blindness throughout the world. Ideally, affected individu...

A Fundamental Study Assessing the Diagnostic Performance of Deep Learning for a Brain Metastasis Detection Task.

PURPOSE: Increased use of deep convolutional neural networks (DCNNs) in medical imaging diagnosis requires determinate evaluation of diagnostic perfor...

Machine learning methods applied to audit of surgical outcomes after treatment for cancer of the head and neck.

Most surgical specialties have attempted to address concerns about unfair comparison of outcomes by "risk-adjusting" data to benchmark specialty-speci...

Artificial intelligence in the interpretation of breast cancer on MRI.

Advances in both imaging and computers have led to the rise in the potential use of artificial intelligence (AI) in various tasks in breast imaging, g...

Inferring the Disease-Associated miRNAs Based on Network Representation Learning and Convolutional Neural Networks.

Identification of disease-associated miRNAs (disease miRNAs) are critical for understanding etiology and pathogenesis. Most previous methods focus on ...

Prediction of Chemotherapy Response of Osteosarcoma Using Baseline F-FDG Textural Features Machine Learning Approaches with PCA.

PURPOSE: Patients with high-grade osteosarcoma undergo several chemotherapy cycles before surgical intervention. Response to chemotherapy, however, is...

Recurrent Saliency Transformation Network for Tiny Target Segmentation in Abdominal CT Scans.

We aim at segmenting a wide variety of organs, including tiny targets (e.g., adrenal gland), and neoplasms (e.g., pancreatic cyst), from abdominal CT ...