Leukemia

A review on the application of deep learning for CT reconstruction, bone segmentation and surgical planning in oral and maxillofacial surgery.

Computer-assisted surgery (CAS) allows clinicians to personalize treatments and surgical interventions and has therefore become an increasingly popular treatment modality in maxillofacial surgery. The current maxillofacial CAS consists of three main steps: (1) CT image reconstruction, (2) bone segmentation, and (3) surgical planning. However, each of these three steps can introduce errors that can...

Discrete protein metric (DPM): A new image similarity metric to calculate accuracy of deep learning-generated cell focal adhesion predictions.

Understanding cell behaviors can provide new knowledge on the development of different pathologies. Focal adhesion (FA) sites are important sub-cellular structures that are involved in these processes. To better facilitate the study of FA sites, deep learning (DL) can be used to predict FA site morphology based on limited microscopic datasets (e.g., cell membrane images). However, calculating the ...

Machine Learning-Based Ultrasound Radiomics for Evaluating the Function of Transplanted Kidneys.

The aim of the study described here was to investigate the value of different machine learning models based on the clinical and radiomic features of 2...

Feature Subset Selection with Optimal Adaptive Neuro-Fuzzy Systems for Bioinformatics Gene Expression Classification.

Recently, bioinformatics and computational biology-enabled applications such as gene expression analysis, cellular restoration, medical image processi...

Deep Learning-Based Computed Tomography Perfusion Imaging to Evaluate the Effectiveness and Safety of Thrombolytic Therapy for Cerebral Infarct with Unknown Time of Onset.

This study was aimed to discuss the effectiveness and safety of deep learning-based computed tomography perfusion (CTP) imaging in the thrombolytic th...

Cluster-Then-Classify Methodology for the Identification of Pain Episodes in Chronic Diseases.

Chronic diseases benefit of the advances on personalize medicine coming out of the integrative convergence of significant developments in systems biol...

Improving the Accuracy of Ensemble Machine Learning Classification Models Using a Novel Bit-Fusion Algorithm for Healthcare AI Systems.

Healthcare AI systems exclusively employ classification models for disease detection. However, with the recent research advances into this arena, it h...

A Multiobjective Evolutionary Nonlinear Ensemble Learning With Evolutionary Feature Selection for Silicon Prediction in Blast Furnace.

In the blast furnace ironmaking process, accurate prediction of silicon content in molten iron is of great significance for maintaining stable furnace...

Target Convergence Analysis of Cancer-Inspired Swarms for Early Disease Diagnosis and Targeted Collective Therapy.

Sensing and perception is generally a challenging aspect of decision-making. In the nanoscale, however, these processes face further complications due...

Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method.

White blood cells (WBCs) are blood cells that fight infections and diseases as a part of the immune system. They are also known as "defender cells." B...

Implementing Critical Machine Learning (ML) Approaches for Generating Robust Discriminative Neuroimaging Representations Using Structural Equation Model (SEM).

Critical ML or CML is a critical approach development of the standard ML (SML) procedure. Conventional ML (ML) is being used in radiology departments ...

DGCyTOF: Deep learning with graphic cluster visualization to predict cell types of single cell mass cytometry data.

Single-cell mass cytometry, also known as cytometry by time of flight (CyTOF) is a powerful high-throughput technology that allows analysis of up to 5...

Automated prediction of the neoadjuvant chemotherapy response in osteosarcoma with deep learning and an MRI-based radiomics nomogram.

OBJECTIVES: To implement a pipeline to automatically segment the ROI and to use a nomogram integrating the MRI-based radiomics score and clinical vari...

CIPHER-SC: Disease-Gene Association Inference Using Graph Convolution on a Context-Aware Network With Single-Cell Data.

Inference of disease-gene associations helps unravel the pathogenesis of diseases and contributes to the treatment. Although many machine learning-bas...

Deep learning based on hematoxylin-eosin staining outperforms immunohistochemistry in predicting molecular subtypes of gastric adenocarcinoma.

In gastric cancer (GC), there are four molecular subclasses that indicate whether patients respond to chemotherapy or immunotherapy, according to the ...

Categorized contrast enhanced mammography dataset for diagnostic and artificial intelligence research.

Contrast-enhanced spectral mammography (CESM) is a relatively recent imaging modality with increased diagnostic accuracy compared to digital mammograp...

A retrospective analysis using deep-learning models for prediction of survival outcome and benefit of adjuvant chemotherapy in stage II/III colorectal cancer.

PURPOSE: Most of Stage II/III colorectal cancer (CRC) patients can be cured by surgery alone, and only certain CRC patients benefit from adjuvant chem...

A deep learning-based approach to automatic proximal femur segmentation in quantitative CT images.

Automatic CT segmentation of proximal femur has a great potential for use in orthopedic diseases, especially in the imaging-based assessments of hip f...

Deep Learning-Based Classification for Melanoma Detection Using XceptionNet.

Skin cancer is one of the most common types of cancer in the world, accounting for at least 40% of all cancers. Melanoma is considered as the 19th mos...

Evaluation of Nano-curcumin effects against Tartrazine-induced abnormalities in liver and kidney histology and other biochemical parameters.

In the current study, 40 albino male rats were investigated to evaluate the impact of Nano-curcumin (Nano-CUR) administration against Tartrazine (TZ)-...