Oncology/Hematology

Convolutional neural networks for multi-class brain disease detection using MRI images.

The brain disorders may cause loss of some critical functions such as thinking, speech, and movement. So, the early detection of brain diseases may help to get the timely best treatment. One of the conventional methods used to diagnose these disorders is the magnetic resonance imaging (MRI) technique. Manual diagnosis of brain abnormalities is time-consuming and difficult to perceive the minute ch...

Segmentation and Diagnosis of Liver Carcinoma Based on Adaptive Scale-Kernel Fuzzy Clustering Model for CT Images.

Medical image analysis plays an important role in computer-aided liver-carcinoma diagnosis. Aiming at the existing image fuzzy clustering segmentation being not suitable to segment CT image with non-uniform background, a fast robust kernel space fuzzy clustering segmentation algorithm is proposed. Firstly, the sample in euclidean space is mapped into the high dimensional feature space through the ...

De novo Gastrinoma: A Case Report.

Gastrinomas are neuroendocrine tumors characterized by gastrin overexpression - 80% are sporadic and 20% are associated with multiple endocrine neopla...

Two stage residual CNN for texture denoising and structure enhancement on low dose CT image.

BACKGROUND AND OBJECTIVE: X-ray computed tomography (CT) plays an important role in modern medical science. Human health problems caused by CT radiati...

Generic Isolated Cell Image Generator.

Building automated cancer screening systems based on image analysis is currently a hot topic in computer vision and medical imaging community. One of ...

Compendiums of cancer transcriptomes for machine learning applications.

There are massive transcriptome profiles in the form of microarray. The challenge is that they are processed using diverse platforms and preprocessing...

Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy.

Gaps in colonoscopy skills among endoscopists, primarily due to experience, have been identified, and solutions are critically needed. Hence, the deve...

Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening.

We present a deep convolutional neural network for breast cancer screening exam classification, trained, and evaluated on over 200000 exams (over 1000...

GSIAR: gene-subcategory interaction-based improved deep representation learning for breast cancer subcategorical analysis using gene expression, applicable for precision medicine.

Tumor subclass detection and diagnosis is inevitable requirement for personalized medical treatment and refinement of the effects that the somatic cel...

Optimizing robot motion for robotic ultrasound-guided radiation therapy.

An important aspect of robotic radiation therapy is active compensation of target motion. Recently, ultrasound has been proposed to obtain real-time v...

[Prediction of chemotherapy response in primary osteosarcoma using the machine learning technique on radiomic data].

INTRODUCTION: Osteosarcoma is the most common malignant bone tumor before 25 years of age. Response to neoadjuvant chemotherapy determines continuatio...

Extracting comprehensive clinical information for breast cancer using deep learning methods.

OBJECTIVE: Breast cancer is the most common malignant tumor among women. The diagnosis and treatment information of breast cancer patients is abundant...

A machine learning algorithm predicts molecular subtypes in pancreatic ductal adenocarcinoma with differential response to gemcitabine-based versus FOLFIRINOX chemotherapy.

PURPOSE: Development of a supervised machine-learning model capable of predicting clinically relevant molecular subtypes of pancreatic ductal adenocar...

Computer-Aided Diagnosis in Histopathological Images of the Endometrium Using a Convolutional Neural Network and Attention Mechanisms.

Uterine cancer (also known as endometrial cancer) can seriously affect the female reproductive system, and histopathological image analysis is the gol...

Interval between planning and frameless stereotactic radiosurgery for brain metastases: are our margins still accurate?

BACKGROUND: Advances in intracranial stereotactic radiosurgery (SRS) have led to dramatically reduced planning target volume (PTV) margins. However, t...

Endoscopic detection and differentiation of esophageal lesions using a deep neural network.

BACKGROUND AND AIMS: Diagnosing esophageal squamous cell carcinoma (SCC) depends on individual physician expertise and may be subject to interobserver...

A novel machine-learning-derived genetic score correlates with measurable residual disease and is highly predictive of outcome in acute myeloid leukemia with mutated NPM1.

Machine-based detection and classification for bone marrow aspirate differential counts: initial development focusing on nonneoplastic cells.

Bone marrow aspirate (BMA) differential cell counts (DCCs) are critical for the classification of hematologic disorders. While manual counts are consi...

A new approach for brain tumor diagnosis system: Single image super resolution based maximum fuzzy entropy segmentation and convolutional neural network.

Magnetic resonance imaging (MRI) images can be used to diagnose brain tumors. Thanks to these images, some methods have so far been proposed in order ...

LOTUS: A single- and multitask machine learning algorithm for the prediction of cancer driver genes.

Cancer driver genes, i.e., oncogenes and tumor suppressor genes, are involved in the acquisition of important functions in tumors, providing a selecti...