Hematology

Lymphoma

Latest AI and machine learning research in lymphoma for healthcare professionals.

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Deep Learning-Based Energy Expenditure Estimation in Assisted and Non-Assisted Gait Using Inertial, EMG, and Heart Rate Wearable Sensors.

Energy expenditure is a key rehabilitation outcome and is starting to be used in robotics-based reha...

CTRR-ncRNA: A Knowledgebase for Cancer Therapy Resistance and Recurrence Associated Non-coding RNAs.

Cancer therapy resistance and recurrence (CTRR) are the dominant causes of death in cancer patients....

Differentiation of eosinophilic and non-eosinophilic chronic rhinosinusitis on preoperative computed tomography using deep learning.

OBJECTIVES: This study aimed to develop deep learning (DL) models for differentiating between eosino...

Robotic Non-Destructive Testing.

Non-destructive testing (NDT) and evaluation (NDE) are commonly referred to as the vast group of ana...

PINC: A Tool for Non-Coding RNA Identification in Plants Based on an Automated Machine Learning Framework.

There is evidence that non-coding RNAs play significant roles in the regulation of nutrient homeosta...

Development of non-bias phenotypic drug screening for cardiomyocyte hypertrophy by image segmentation using deep learning.

The number of patients with heart failure and related deaths is rapidly increasing worldwide, making...

Protocol for fast scRNA-seq raw data processing using scKB and non-arbitrary quality control with COPILOT.

We describe a protocol to perform fast and non-arbitrary quality control of single-cell RNA sequenci...

Synchronization control of time-delay neural networks via event-triggered non-fragile cost-guaranteed control.

This paper is devoted to event-triggered non-fragile cost-guaranteed synchronization control for tim...

A radiomics feature-based machine learning models to detect brainstem infarction (RMEBI) may enable early diagnosis in non-contrast enhanced CT.

OBJECTIVES: Magnetic resonance imaging has high sensitivity in detecting early brainstem infarction ...

Adaptive graph convolutional clustering network with optimal probabilistic graph.

The graph convolutional network (GCN)-based clustering approaches have achieved the impressive perfo...

Deep Learning-Based Nuclear Morphometry Reveals an Independent Prognostic Factor in Mantle Cell Lymphoma.

Blastoid/pleomorphic morphology is associated with short survival in mantle cell lymphoma (MCL), but...

Deep learning-based tumor microenvironment segmentation is predictive of tumor mutations and patient survival in non-small-cell lung cancer.

BACKGROUND: Despite the fact that tumor microenvironment (TME) and gene mutations are the main deter...

A Concise Yet Effective Model for Non-Aligned Incomplete Multi-View and Missing Multi-Label Learning.

In reality, learning from multi-view multi-label data inevitably confronts three challenges: missing...

Vibro-Acoustic Distributed Sensing for Large-Scale Data-Driven Leak Detection on Urban Distribution Mains.

Non-surfacing leaks constitute the dominant source of water losses for utilities worldwide. This pap...

Deep-TOF-PET: Deep learning-guided generation of time-of-flight from non-TOF brain PET images in the image and projection domains.

We aim to synthesize brain time-of-flight (TOF) PET images/sinograms from their corresponding non-TO...

A novel combined deep learning methodology to non-invasively estimate hemoglobin levels in blood with high accuracy.

Hemoglobin is an essential protein found in blood and should not fall below a certain level in human...

DeepMTS: Deep Multi-Task Learning for Survival Prediction in Patients With Advanced Nasopharyngeal Carcinoma Using Pretreatment PET/CT.

Nasopharyngeal Carcinoma (NPC) is a malignant epithelial cancer arising from the nasopharynx. Surviv...

Non-small cell lung cancer diagnosis aid with histopathological images using Explainable Deep Learning techniques.

BACKGROUND: Lung cancer has the highest mortality rate in the world, twice as high as the second hig...

Low precision decentralized distributed training over IID and non-IID data.

Decentralized distributed learning is the key to enabling large-scale machine learning (training) on...

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