Digital Imaging for Blood Diseases

Decoupling Detection and Classification to Improve Morphological Phenotype Analysis of Sickle Red Blood Cells in Full-Scope Microscopy

This paper improves sickle cell disease morphology analysis by combining a YOLO detector with a DenseNet classifier for accurate detection and classification of red blood cell morphologies in full-scope microscopy images.

Plasmo3Net: A Convolutional Neural Network-Based Algorithm for Detecting Malaria Parasites in Thin Blood Smear Images

This paper develops Plasmo3Net, a CNN-based algorithm for detecting malaria parasites in thin blood smears, achieving high accuracy and precision.

Majority-LCL: Towards Malaria Cell Detection using Label Contrastive Learning and Majority voting Ensembling

This paper introduces a malaria cell detection method combining label contrastive learning and majority voting ensembling to improve diagnostic accuracy using microscopic images.

BoMBR: An Annotated Bone Marrow Biopsy Dataset for Segmentation of Reticulin Fibers

The paper introduces BoMBR, a dataset of 201 annotated bone marrow biopsy images for reticulin fiber segmentation, and demonstrates a deep learning model achieving high accuracy in reticulin quantification and marrow fibrosis grading.

BaMBo: An Annotated Bone Marrow Biopsy Dataset for Segmentation Task

The paper introduces BaMBo, a bone marrow biopsy dataset for automated cellularity calculation, and presents a U-Net model achieving 83.1% Dice score and 5.9% cellularity prediction error.

Automatic Classification of Circulating Blood Cell Clusters based on Multi-channel Flow Cytometry Imaging

This study introduces a framework for automatically classifying circulating blood cell clusters using flow cytometry images, achieving over 95% accuracy in cluster classification and phenotype identification.

Leveraging Sparse Annotations for Leukemia Diagnosis on the Large Leukemia Dataset

This paper introduces a large leukemia dataset and methods for WBC detection with attribute analysis using sparse annotations to improve diagnostic accuracy and reduce annotation burden.

Application of machine learning in a rodent malaria model for rapid, accurate, and consistent parasite counts

The paper develops a machine learning tool for automated parasite counting in rodent malaria models, achieving high accuracy and consistency across labs.

Real-time detection of acute lymphoblastic leukemia cells using deep learning

This study uses deep learning models to detect acute lymphoblastic leukemia cells in microscopic images, achieving 97.2% accuracy and 80 frames per second processing speed.

Detection of live breast cancer cells in brightfield microscopy images containing white blood cells by image analysis and deep learning

This paper develops deep learning methods to detect unstained breast cancer cells in brightfield images containing white blood cells, achieving high sensitivity and specificity.

Label-free assessment of red blood cell storage lesions by deep learning

This study uses deep learning and label-free imaging flow cytometry to classify red blood cell morphologies during storage, achieving 90% accuracy and predicting expiration dates based on morphological changes.

Classification of human white blood cells using machine learning for stain-free imaging flow cytometry

This study compares classical machine learning and deep learning for classifying white blood cells using stain-free imaging flow cytometry, achieving balanced accuracies of 0.776 and 0.697 for white blood cells and 0.866 and 0.867 for eosinophils.