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
Leveraging Machine Learning and Deep Learning for Advanced Malaria Detection Through Blood Cell Images

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Pathology-Aware Prototype Evolution via LLM-Driven Semantic Disambiguation for Multicenter Diabetic Retinopathy Diagnosis

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Impact of magnification on deep learning approaches through comprehensive comparative study of histopathological breast cancer classification

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A computer-assisted medical diagnosis system for cancer diseases based on quaternion orthogonal Rademacher-Fourier moments and deep learning

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Study of Retinal Vessel Segmentation Algorithm Based on Receptive Field Expansion and Feature Refinement

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Artificial intelligence coupled to pharmacometrics modelling to tailor malaria and tuberculosis treatment in Africa

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Towards accurate and interpretable brain tumor diagnosis: T-FSPANNet with Tri-Attribute and pyramidal attention-based feature fusion

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Artificial intelligence and machine learning in infectious disease diagnostics: a comprehensive review of applications, challenges, and future directions

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Two-dimensional correlation spectroscopy (2D-COS) in the analysis of parasitemia in Raman spectra of red blood cells of patients diagnosed with malaria

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Comparative analysis of deep learning and machine learning techniques for forecasting new malaria cases in Cameroon’s Adamaoua region

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

article Full Text OpenAlex 691 FWCI125.11544617

A visual-language foundation model for computational pathology

article Full Text OpenAlex 472 FWCI300.86469664

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

article Full Text OpenAlex 427 FWCI109.07411302

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

article Full Text OpenAlex 362 FWCI42.08470424

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

article Full Text OpenAlex 326 FWCI208.24180701

2022 26th International Conference on Pattern Recognition (ICPR)

paratext Full Text OpenAlex 323 FWCI0

An Introduction to Convolutional Neural Networks

article Full Text OpenAlex 281 FWCI29.09107327

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

article Full Text OpenAlex 281 FWCI148.97427422

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

article Full Text OpenAlex 245 FWCI32.83374211

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

review Full Text OpenAlex 238 FWCI46.20852159