A report claiming 95.12% few-shot accuracy on Mini-ImageNet from a self-supervised ResNet-101 pipeline, with insufficient experimental evidence.
Convolutional Neural Networks for Predictive Modeling of Lung Disease
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abstract
In this paper, Pro-HRnet-CNN, an innovative model combining HRNet and void-convolution techniques, is proposed for disease prediction under lung imaging. Through the experimental comparison on the authoritative LIDC-IDRI dataset, we found that compared with the traditional ResNet-50, Pro-HRnet-CNN showed better performance in the feature extraction and recognition of small-size nodules, significantly improving the detection accuracy. Particularly within the domain of detecting smaller targets, the model has exhibited a remarkable enhancement in accuracy, thereby pioneering an innovative avenue for the early identification and prognostication of pulmonary conditions.
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Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification
A report claiming 95.12% few-shot accuracy on Mini-ImageNet from a self-supervised ResNet-101 pipeline, with insufficient experimental evidence.