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
Classification of Multiple Eye Diseases, Parallel Feature Extraction with Transfer Learning

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Applications of Artificial Intelligence in Veterinary Anatomical Pathology: Enhancing Diagnosis and Treatment in Animal Healthcare

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Star Classifier Head on Deformable Attention Vision Transformer for Small Datasets

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Artificial Intelligence in Hematology

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Attention Scale Fusion Network for Qualitative and Quantitative Analysis of Serum Tumor Biomarkers Via Label-Free Surface-Enhanced Raman Spectroscopy

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A new limestone-associated species of wolf snake in the Lycodon fasciatus complex (Squamata: Serpentes: Colubridae) from central Vietnam

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BIOMEDICA: An Open Biomedical Image-Caption Archive, Dataset, and Vision-Language Models Derived from Scientific Literature

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Prediction and Classification for Melanoma Using Hybrid Learning Technique and Multi-Features

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Prototype-Based Image Prompting for Weakly Supervised Histopathological Image Segmentation

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Full Conformal Adaptation of Medical Vision-Language Models

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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 569 FWCI63.761

2020 25th International Conference on Pattern Recognition (ICPR)

paratext Full Text OpenAlex 452 FWCI0

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

article Full Text OpenAlex 397 FWCI32.061

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

article Full Text OpenAlex 332 FWCI64.344

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

article Full Text OpenAlex 319 FWCI29.641

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

article Full Text OpenAlex 301 FWCI63.697

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

preprint Full Text OpenAlex 300 FWCI0

2022 26th International Conference on Pattern Recognition (ICPR)

paratext Full Text OpenAlex 293 FWCI0

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

article Full Text OpenAlex 257 FWCI31.512

An Introduction to Convolutional Neural Networks

article Full Text OpenAlex 256 FWCI24.286