AIMC Topic: Leukemia

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Automatic recognition of different types of acute leukaemia in peripheral blood by image analysis.

Journal of clinical pathology
AIMS: Morphological differentiation among different blast cell lineages is a difficult task and there is a lack of automated analysers able to recognise these abnormal cells. This study aims to develop a machine learning approach to predict the diagn...

Evaluating classification accuracy for modern learning approaches.

Statistics in medicine
Deep learning neural network models such as multilayer perceptron (MLP) and convolutional neural network (CNN) are novel and attractive artificial intelligence computing tools. However, evaluation of the performance of these methods is not readily av...

Optimal two-stage dynamic treatment regimes from a classification perspective with censored survival data.

Biometrics
Clinicians often make multiple treatment decisions at key points over the course of a patient's disease. A dynamic treatment regime is a sequence of decision rules, each mapping a patient's observed history to the set of available, feasible treatment...

Evaluation of a Machine Learning-Based Prognostic Model for Unrelated Hematopoietic Cell Transplantation Donor Selection.

Biology of blood and marrow transplantation : journal of the American Society for Blood and Marrow Transplantation
The survival of patients undergoing hematopoietic cell transplantation (HCT) from unrelated donors for acute leukemia exhibits considerable variation, even after stringent genetic matching. To improve the donor selection process, we attempted to crea...

Leukocyte Image Segmentation Using Novel Saliency Detection Based on Positive Feedback of Visual Perception.

Journal of healthcare engineering
This paper presents a novel method for salient object detection in nature image by simulating microsaccades in fixational eye movements. Due to a nucleated cell usually stained that is salient obviously, the proposed method is suitable to segment nuc...

Sensitive detection of rare disease-associated cell subsets via representation learning.

Nature communications
Rare cell populations play a pivotal role in the initiation and progression of diseases such as cancer. However, the identification of such subpopulations remains a difficult task. This work describes CellCnn, a representation learning approach to de...

Ensemble Feature Learning of Genomic Data Using Support Vector Machine.

PloS one
The identification of a subset of genes having the ability to capture the necessary information to distinguish classes of patients is crucial in bioinformatics applications. Ensemble and bagging methods have been shown to work effectively in the proc...

An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images.

Scientific reports
This research proposes an intelligent decision support system for acute lymphoblastic leukaemia diagnosis from microscopic blood images. A novel clustering algorithm with stimulating discriminant measures (SDM) of both within- and between-cluster sca...

An AI-based automatic leukemia classification system utilizing dimensional Archimedes optimization.

Scientific reports
Leukemia is a common type of blood cancer marked by the abnormal and uncontrolled proliferation and expansion of white blood cells. This anomaly impacts the blood and bone marrow, diminishing the bone marrow's capacity to generate platelets and red b...