Sep 28, 2022 · In this paper, we propose a deeply supervised Layer Selective Attention Network (LSANet), which comprehensively uses label information in feature-level and ...
A deeply supervised Layer Selective Attention Network (LSANet) is proposed, which comprehensively uses label information in feature-level and ...
LSA introduces a weight allocation scheme which can dynamically adjust the weighting factor of each auxiliary branch during the whole training process to ...
Sep 28, 2022 · In this paper, We revisit the DSL methods and analyze the effectiveness of DSL for medical image classification tasks. For further enhancement ...
Sep 28, 2022 · Deeply Supervised Layer Selective Attention Network: Towards Label-Efficient Learning for Medical Image Classification.
In this paper, we propose Deeply-supervised Knowledge Synergy (DKS), a new method aiming to train CNNs with improved generalization ...
Deeply-supervised Knowledge Synergy is proposed, a new method aiming to train CNNs with improved generalization ability for image classification tasks ...
People also ask
Which CNN model is best for medical image classification?
Which deep learning image classification is best?
In this survey, we extensively investigated over 300 recent papers to provide a comprehensive overview of recent progress on label-efficient learning ...
标记医学图像取决于专业知识，因此很难在短时间内以高质量获取大量注释的医学图像。因此，在小型数据集中充分利用有限标记的样品来构建高性能模型是医疗图像分类问题的 ...
Feb 20, 2023 · This study proposes a fully automatic method called the selective deeply supervised multi-scale attention network (SDS-MSA-Net) for segmenting brain tumor ...