Lymphoma

Oropharyngeal squamous cell carcinoma: radiomic machine-learning classifiers from multiparametric MR images for determination of HPV infection status.

We investigated the ability of machine-learning classifiers on radiomics from pre-treatment multiparametric magnetic resonance imaging (MRI) to accurately predict human papillomavirus (HPV) status in patients with oropharyngeal squamous cell carcinoma (OPSCC). This retrospective study collected data of 60 patients (48 HPV-positive and 12 HPV-negative) with newly diagnosed histopathologically prove...

Prognostic value of FDG-PET radiomics with machine learning in pancreatic cancer.

Patients with pancreatic cancer have a poor prognosis, therefore identifying particular tumor characteristics associated with prognosis is important. This study aims to investigate the utility of radiomics with machine learning using F-fluorodeoxyglucose (FDG)-PET in patients with pancreatic cancer. We enrolled 161 patients with pancreatic cancer underwent pretreatment FDG-PET/CT. The area of the ...

FUSI-CAD: Coronavirus (COVID-19) diagnosis based on the fusion of CNNs and handcrafted features.

The precise and rapid diagnosis of coronavirus (COVID-19) at the very primary stage helps doctors to manage patients in high workload conditions. In a...

The data dimensionality reduction and bad data detection in the process of smart grid reconstruction through machine learning.

To detect false data injection attacks (FDIAs) in power grid reconstruction and solve the problem of high data dimension and bad abnormal data process...

Recognition of Non-Manual Content in Continuous Japanese Sign Language.

The quality of recognition systems for continuous utterances in signed languages could be largely advanced within the last years. However, research ef...

Preoperative Prediction of Lymph Node Metastasis from Clinical DCE MRI of the Primary Breast Tumor Using a 4D CNN.

In breast cancer, undetected lymph node metastases can spread to distal parts of the body for which the 5-year survival rate is only 27%, making accur...

Artificial intelligence to detect MYC translocation in slides of diffuse large B-cell lymphoma.

In patients with suspected lymphoma, the tissue biopsy provides lymphoma confirmation, classification, and prognostic factors, including genetic chang...

The path to international medals: A supervised machine learning approach to explore the impact of coach-led sport-specific and non-specific practice.

Research investigating the nature and scope of developmental participation patterns leading to international senior-level success is mainly explorativ...

Deep learning radiomics of ultrasonography: Identifying the risk of axillary non-sentinel lymph node involvement in primary breast cancer.

BACKGROUND: Completion axillary lymph node dissection is overtreatment for patients with sentinel lymph node (SLN) metastasis in whom the metastatic r...

Lymph node metastasis prediction of papillary thyroid carcinoma based on transfer learning radiomics.

Non-invasive assessment of the risk of lymph node metastasis (LNM) in patients with papillary thyroid carcinoma (PTC) is of great value for the treatm...

Classification of Non-Severe Traumatic Brain Injury from Resting-State EEG Signal Using LSTM Network with ECOC-SVM.

Traumatic brain injury (TBI) is one of the common injuries when the human head receives an impact due to an accident or fall and is one of the most fr...

Multi-domain convolutional neural network (MD-CNN) for radial reconstruction of dynamic cardiac MRI.

PURPOSE: Cardiac MR cine imaging allows accurate and reproducible assessment of cardiac function. However, its long scan time not only limits the spat...

Democratizing AI: non-expert design of prediction tasks.

Non-experts have long made important contributions to machine learning (ML) by contributing training data, and recent work has shown that non-experts ...

Preliminary clinical assessment of dynamic carbon-11 methionine positron-emission tomography/computed tomography for the diagnosis of the pathologies in patients with musculoskeletal lesions: a prospective study.

BACKGROUND: This study prospectively assessed the diagnostic capacity of dynamic carbon-11 methionine (C-11 MET) positron-emission tomography (PET)/co...

The deep learning model combining CT image and clinicopathological information for predicting ALK fusion status and response to ALK-TKI therapy in non-small cell lung cancer patients.

PURPOSE: This study aimed to investigate the deep learning model (DLM) combining computed tomography (CT) images and clinicopathological information f...

Editorial for: "Primary Central Nervous System Lymphoma: Clinical Evaluation of Automated Segmentation on Multiparametric MRI Using Deep Learning".

Automatic segmentation of brain MRI using a novel patch-wise U-net deep architecture.

Accurate segmentation of brain magnetic resonance imaging (MRI) is an essential step in quantifying the changes in brain structure. Deep learning in r...

Deep learning for cerebral angiography segmentation from non-contrast computed tomography.

Cerebral computed tomography angiography is a widely available imaging technique that helps in the diagnosis of vascular pathologies. Contrast adminis...

Motion correction of respiratory-gated PET images using deep learning based image registration framework.

Artifacts caused by patient breathing and movement during PET data acquisition affect image quality. Respiratory gating is commonly used to gate the l...

Application of a new HMW framework derived ANN model for optimization of aquatic dissolved organic matter removal by coagulation.

Removing dissolved organic matter (DOM) with polyaluminium chloride is one of the primary goals of drinking water treatment. In this study, a new HMW ...