Leukemia

A Survey of Deep Network Techniques All Classifiers Can Adopt.

Deep neural networks (DNNs) have introduced novel and useful tools to the machine learning community...

Robots Are Not All the Same: Young Adults' Expectations, Attitudes, and Mental Attribution to Two Humanoid Social Robots.

The human physical resemblance of humanoid social robots (HRSs) has proven to be particularly effect...

Characterization of specific and distinct patient types in clinical trials of acute schizophrenia using an uncorrelated PANSS score matrix transform (UPSM).

Understanding the specificity of symptom change in schizophrenia can facilitate the evaluation antip...

Piperidine based 1,2,3-triazolylacetamide derivatives induce cell cycle arrest and apoptotic cell death in .

The fungal pathogen , is a serious threat to public health and is associated with bloodstream infec...

A convolutional neural network-based learning approach to acute lymphoblastic leukaemia detection with automated feature extraction.

Leukaemia is a type of blood cancer which mainly occurs when bone marrow produces excess white blood...

Deep learning networks on chronic liver disease assessment with fine-tuning of shear wave elastography image sequences.

Chronic liver disease (CLD) is currently one of the major causes of death worldwide. If not treated,...

Prediction of Cranial Radiotherapy Treatment in Pediatric Acute Lymphoblastic Leukemia Patients Using Machine Learning: A Case Study at MAHAK Hospital.

BACKGROUND: Acute Lymphoblastic Leukemia (ALL) is the most common blood disease in children and is r...

Role of Red Cell Indices in Screening for Beta Thalassemia Trait: an Assessment of the Individual Indices and Application of Machine Learning Algorithm.

Antenatal screening for beta thalassemia trait (BTT) followed by counseling of couples is an efficie...

OrganoidTracker: Efficient cell tracking using machine learning and manual error correction.

Time-lapse microscopy is routinely used to follow cells within organoids, allowing direct study of d...

Comparative Analysis of Cutting Forces, Torques, and Vibration in Drilling of Bovine, Porcine, and Artificial Femur Bone with Considerations for Robot Effector Stiffness.

Bone drilling is known as one of the most sensitive milling processes in biomedical engineering fiel...

Effects of walking distance over robot-assisted training on walking ability in chronic stroke patients.

An understanding of the dose-response during training is important to identify the rehabilitation pr...

Performance of Force-Field- and Machine Learning-Based Scoring Functions in Ranking MAO-B Protein-Inhibitor Complexes in Relevance to Developing Parkinson's Therapeutics.

Monoamine oxidase B (MAOB) is expressed in the mitochondrial membrane and has a key role in degradin...

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 multipar...

Identification and Staging of B-Cell Acute Lymphoblastic Leukemia Using Quantitative Phase Imaging and Machine Learning.

Identification and classification of leukemia cells in a rapid and label-free fashion is clinically ...

Not All Sepsis-Associated Acute Kidney Injury Is the Same: There May Be an App for That.

All Models Are Wrong, but Some Are Useful.

Machine Learning Approaches Reveal Metabolic Signatures of Incident Chronic Kidney Disease in Individuals With Prediabetes and Type 2 Diabetes.

Early and precise identification of individuals with prediabetes and type 2 diabetes (T2D) at risk f...

Leveraging TCGA gene expression data to build predictive models for cancer drug response.

BACKGROUND: Machine learning has been utilized to predict cancer drug response from multi-omics data...

The optimisation of deep neural networks for segmenting multiple knee joint tissues from MRIs.

Automated semantic segmentation of multiple knee joint tissues is desirable to allow faster and more...

One model to rule them all in network science?

One Algorithm May Not Fit All: How Selection Bias Affects Machine Learning Performance.

Machine learning (ML) algorithms have demonstrated high diagnostic accuracy in identifying and categ...