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.
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A visual-language foundation model for computational pathology

article Full Text OpenAlex 798 FWCI182.9217

ResMLP: Feedforward Networks for Image Classification With Data-Efficient Training

article Full Text OpenAlex 794 FWCI77.1444

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

article Full Text OpenAlex 558 FWCI71.2697

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

article Full Text OpenAlex 469 FWCI107.6415

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

conference-paper Full Text OpenAlex 449 FWCI63.8853

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

article Full Text OpenAlex 398 FWCI61.184

2022 26th International Conference on Pattern Recognition (ICPR)

paratext Full Text OpenAlex 323 FWCI0

CellViT: Vision Transformers for precise cell segmentation and classification

article Full Text OpenAlex 303 FWCI68.8538

An Introduction to Convolutional Neural Networks

article Full Text OpenAlex 298 FWCI19.3531

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

article Full Text OpenAlex 277 FWCI19.4594