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
A visual-language foundation model for computational pathology

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A comprehensive study on tuberculosis prediction models: Integrating machine learning into epidemiological analysis

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CA-SegNet: A channel-attention encoder–decoder network for histopathological image segmentation

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Machine learning in the prediction of treatment response in rheumatoid arthritis: A systematic review

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Towards ovarian cancer diagnostics: A vision transformer-based computer-aided diagnosis framework with enhanced interpretability

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Deep CNNs for Diabetic Retinopathy Classification: A Transfer Learning Perspective

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SIM-OFE: Structure Information Mining and Object-Aware Feature Enhancement for Fine-Grained Visual Categorization

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An explainable ensemble approach for advanced brain tumor classification applying Dual-GAN mechanism and feature extraction techniques over highly imbalanced data

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Neural Cellular Automata for Lightweight, Robust and Explainable Classification of White Blood Cell Images

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Enhanced cancer classification and critical feature visualization using Raman spectroscopy and convolutional neural networks

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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 674 FWCI122.5700615

2020 25th International Conference on Pattern Recognition (ICPR)

paratext Full Text OpenAlex 648 FWCI0

TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification

preprint Full Text OpenAlex 498 FWCI0

FAT-Net: Feature adaptive transformers for automated skin lesion segmentation

article Full Text OpenAlex 474 FWCI34.80405587

A visual-language foundation model for computational pathology

article Full Text OpenAlex 411 FWCI261.89920514

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

article Full Text OpenAlex 393 FWCI100.38905484

SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation

article Full Text OpenAlex 372 FWCI45.00679011

A Novel Deep-Learning Model for Automatic Detection and Classification of Breast Cancer Using the Transfer-Learning Technique

article Full Text OpenAlex 370 FWCI39.93231177

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

article Full Text OpenAlex 340 FWCI39.96871353

2022 26th International Conference on Pattern Recognition (ICPR)

paratext Full Text OpenAlex 324 FWCI0