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
Nnmamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Model

article Full Text OpenAlex

AI-assisted ophthalmic imaging for early detection of neurodegenerative diseases

review Full Text OpenAlex

Cellpose-SAM: superhuman generalization for cellular segmentation

preprint Full Text OpenAlex

Lightweight Evolving U-Net for Next-Generation Biomedical Imaging

article Full Text OpenAlex

A machine learning approach for assessing acute infection by erythrocyte sedimentation rate (ESR) kinetics

article Full Text OpenAlex

Domain Generalization in Computational Pathology: Survey and Guidelines

review Full Text OpenAlex

A concept-based interpretable model for the diagnosis of choroid neoplasias using multimodal data

article Full Text OpenAlex

Explainable AI for enhanced accuracy in malaria diagnosis using ensemble machine learning models

article Full Text OpenAlex

Cloud-Based Optimized Deep Learning Framework for Automated Glaucoma Detection Using Stationary Wavelet Transform and Improved Grey-Wolf-Optimization with ELM Approach

article Full Text OpenAlex

Advancements in Neurodegenerative Disease Diagnosis and Prediction: A Machine Learning Approach

book-chapter 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 531 FWCI64.709

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 363 FWCI32.045

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

article Full Text OpenAlex 306 FWCI64.243

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

article Full Text OpenAlex 295 FWCI29.366

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

preprint Full Text OpenAlex 251 FWCI0

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

article Full Text OpenAlex 235 FWCI58.308

An Introduction to Convolutional Neural Networks

article Full Text OpenAlex 231 FWCI23.411

Diabetic retinopathy detection and classification using capsule networks

article Full Text OpenAlex 218 FWCI28.504

Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks

article Full Text OpenAlex 215 FWCI26.803