6 citations · 9 across the 19 of their papers we have counts for
19 papers · 1 filter
Phase-Aware Spatial-Frequency Fusion for Few-Shot Fine-Grained Image Classification
Ruiling Liu, Linyue Zhang, Wenyi Zeng +5
Few-shot fine-grained image classification (FSFGIC) aims to classify similar images with limited labeled examples. This work highlights the critical yet underutilized role of phase…
GloResNet: A lightweight 3D CNN with global topological features for preterm brain injury prediction
Boyu Yuan, Jiamiao Lu, Weichuan Zhang +5
This study introduces an automated deep learning framework for predicting brain injury (BI) in preterm infants from T2-weighted MRI (dHCP dataset). We propose GloResNet, a lightwei…
Frequency-Enhanced Dual-Subspace Networks for Few-Shot Fine-Grained Image Classification
Meijia Wang, Guochao Wang, Haozhen Chu +4
Few-shot fine-grained image classification aims to recognize subcategories with high visual similarity using only a limited number of annotated samples. Existing metric learning-ba…
Adaptive receptive field-based spatial-frequency feature reconstruction network for fine-grained few-shot image classification
Linyue Zhang, Wenyi Zeng, Zicheng Pan +6
Feature reconstruction techniques are widely applied for few-shot fine-grained image classification (FSFGIC). Our research indicates that one of the main challenges facing existing…
Meningioma Analysis and Diagnosis using Limited Labeled Samples
Jiamiao Lu, Wei Wu, Ke Gao +8
The biological behavior and treatment response of meningiomas depend on their grade, making an accurate diagnosis essential for treatment planning and prognosis assessment. We obse…
Deep learning-based neurodevelopmental assessment in preterm infants
Lexin Ren, Jiamiao Lu, Weichuan Zhang +6
Preterm infants (born between 28 and 37 weeks of gestation) face elevated risks of neurodevelopmental delays, making early identification crucial for timely intervention. While dee…