3D CNNs predict elastic moduli of nanoporous metals with R²=0.955, outperforming descriptor-based models, and transfer learning works on smaller denser datasets for large-scale Pareto optimization.
In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3representative citing papers
A three-stage framework combines dual-head CNNs, saliency attribution, neuroanatomical atlas mapping, and LLMs to generate interpretable reports for brain tumor classification on MRI images.
MS-SSE-Net integrates multi-scale feature extraction and squeeze-and-excitation attention into DenseNet201, reaching 99.26% accuracy on the StructDamage dataset and outperforming the baseline by about 0.73 percentage points.
citing papers explorer
-
Transferable 3D Convolutional Neural Networks for Elastic Constants Prediction in Nanoporous Metals
3D CNNs predict elastic moduli of nanoporous metals with R²=0.955, outperforming descriptor-based models, and transfer learning works on smaller denser datasets for large-scale Pareto optimization.
-
Bridging visual saliency and large language models for explainable deep learning in medical imaging
A three-stage framework combines dual-head CNNs, saliency attribution, neuroanatomical atlas mapping, and LLMs to generate interpretable reports for brain tumor classification on MRI images.
-
MS-SSE-Net: A Multi-Scale Spatial Squeeze-and-Excitation Network for Structural Damage Detection in Civil and Geotechnical Engineering
MS-SSE-Net integrates multi-scale feature extraction and squeeze-and-excitation attention into DenseNet201, reaching 99.26% accuracy on the StructDamage dataset and outperforming the baseline by about 0.73 percentage points.