专题:Digital Imaging for Blood Diseases

This cluster of papers focuses on the automated analysis of blood cell images, particularly in the context of detecting malaria parasites and classifying leukemia. The research utilizes techniques such as image processing, convolutional neural networks, and machine learning for tasks including white blood cell segmentation, feature extraction, and automated diagnosis from microscopic blood images.
最新文献
CellVoyager: AI CompBio agent generates new insights by autonomously analyzing biological data

article Full Text OpenAlex

Deep learning on histopathological images to predict breast cancer recurrence risk and chemotherapy benefit: a multicentre, model development and validation study

article Full Text OpenAlex

A Unified CNN-Based Instance Segmentation Architecture for Blood Cell Classification and Early Cancer Abnormality Recognition

article Full Text OpenAlex

Machine Learning Based Multi Criteria Decision Analysis for Lymphoma Diagnosis.

article Full Text OpenAlex

Classification of Overlapping Red Blood Cells in Microscopic Blood Smear Images Using Deep Learning

article Full Text OpenAlex

Multiscale contextual attention network for robust diagnosis of acute lymphoblastic leukemia in blood smears: Implications for clinical adoption and the role of medical science liaisons

article Full Text OpenAlex

Not Only Grey Matter: OmniBrain for Robust Multimodal Classification of Alzheimer's Disease

article Full Text OpenAlex

Diversity over scale: Whole-slide image variety enables H&E foundation model training with fewer patches

article Full Text OpenAlex

A DenseNet-YOLOv8 Fusion Model for Intelligent Parasite Egg Detection and Classification

book-chapter Full Text OpenAlex

Ultrasensitive microfluidic detection of red blood cell deformability: Age-related decline in deformability

article Full Text OpenAlex

近5年高被引文献
ResMLP: Feedforward Networks for Image Classification With Data-Efficient Training

article Full Text OpenAlex 748 FWCI91.6311

A visual-language foundation model for computational pathology

article Full Text OpenAlex 636 FWCI204.7807

The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification

article Full Text OpenAlex 484 FWCI80.9866

ASF-YOLO: A novel YOLO model with attentional scale sequence fusion for cell instance segmentation

article Full Text OpenAlex 415 FWCI133.8331

DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification

article Full Text OpenAlex 404 FWCI39.0906

Accurate leukocyte detection based on deformable-DETR and multi-level feature fusion for aiding diagnosis of blood diseases

article Full Text OpenAlex 344 FWCI78.9712

2022 26th International Conference on Pattern Recognition (ICPR)

paratext Full Text OpenAlex 323 FWCI0

An Introduction to Convolutional Neural Networks

article Full Text OpenAlex 293 FWCI24.1738

Brain Tumor Analysis Using Deep Learning and VGG-16 Ensembling Learning Approaches

article Full Text OpenAlex 267 FWCI21.2602

A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches

review Full Text OpenAlex 262 FWCI33.1816