A quantum convolutional network and ResNet (50)-based classification architecture for the MNIST medical dataset
Biomedical image classification is crucial for both computer vision tasks and clinical care.
The conventional method requires a significant amount of time and effort for extracting and …
The conventional method requires a significant amount of time and effort for extracting and …
AMIAC: adaptive medical image analyzes and classification, a robust self-learning framework
Adaptive self-learning is a promising technique in medical image analysis that enables deep
learning models to adapt to changes in image distribution over time. As medical image data …
learning models to adapt to changes in image distribution over time. As medical image data …
Complex mixer for medmnist classification decathlon
With the development of the medical image field, researchers seek to develop a class of
datasets to block the need for medical knowledge, such as\text {MedMNIST}(v2). MedMNIST …
datasets to block the need for medical knowledge, such as\text {MedMNIST}(v2). MedMNIST …
[HTML][HTML] A knowledge-based learning framework for self-supervised pre-training towards enhanced recognition of biomedical microscopy images
W Chen, C Li, D Chen, X Luo - Neural Networks, 2023 - Elsevier
Self-supervised pre-training has become the priory choice to establish reliable neural
networks for automated recognition of massive biomedical microscopy images, which are …
networks for automated recognition of massive biomedical microscopy images, which are …
SwinMedNet: Leveraging Swin Transformer for Robust Diabetic Retinopathy Classification from the RetinaMNIST2D Dataset
MM Haque, S Akter, AF Ashrafi - 2024 6th International …, 2024 - ieeexplore.ieee.org
Diabetic retinopathy (DR) is a leading cause of blindness, making early detection crucial.
This study investigates the performance of a Swin Transformer-based deep learning model …
This study investigates the performance of a Swin Transformer-based deep learning model …