RiTex: Harmonization of Radiomic Features Based on Riemannian Geometry [0.03%]
基于黎曼几何的放射组学特征调谐(RiTex)
Darya A Voitenko,Anton V Vladzymyrskyy,Olga V Omelyanskaya et al.
Darya A Voitenko et al.
Batch effects arising from variations in hardware, acquisition protocols, and reconstruction parameters present a critical challenge in radiomics, limiting the generalizability of models across multicentre studies. Existing harmonization me...
Frequency-Guided Cross-Modal Interaction for Multimodal Yeast Classification Based on Light-Scattering and Microscopy Images [0.03%]
基于散射和显微图像的多模态酵母分类的频率引导跨模态交互方法
Zexi Cheng,Xiaoxuan Liu,Shamanth Shankarnarayan et al.
Zexi Cheng et al.
Accurate identification of pathogenic yeasts is essential for clinical diagnosis and effective antifungal therapy. However, current approaches predominantly rely on microscopy-based models, which require large-scale annotated datasets and e...
Laurie S van de Weerd,Nick J van de Berg,L Lucia Rijstenberg et al.
Laurie S van de Weerd et al.
Ovarian cancer (OC) is typically treated with cytoreductive surgery (CRS). Hyperspectral imaging (HSI) is an emerging non-invasive, label-free technique that enables whole-area scanning, making it a promising tool for real-time tumour recog...
SAR-Efficient Sub-Volume Imaging Using Nonlinear Gradient Magnetic Fields [0.03%]
基于非线性梯度磁场的SAR高效子体积成像方法
Emre Kopanoglu,Ergin Atalar,R Todd Constable
Emre Kopanoglu
Excitation using nonlinear gradient magnetic fields is investigated as a means of sub-volume magnetic resonance imaging (MRI). Conventional gradient fields provide encoding along a single direction, whereas nonlinear gradient fields encode ...
FSSM: Frequency-Enhanced State Space Modeling with FFT-Based Two-Sided Non-Causal Convolution for Image Dehazing [0.03%]
基于FFT的双侧非因果卷积的频率增强状态空间图像去雾模型(FSSM)
Li Zeng,Yinqing Huang
Li Zeng
Image dehazing is a fundamental visual restoration task for improving visual perception under low-visibility weather conditions, especially in UAV-based remote sensing, traffic monitoring, and surveillance scenarios. Existing convolutional ...
Renfei Li,Mingxiu Lin
Renfei Li
Surface defect detection is an important task for quality assurance in steel manufacturing. Although YOLO-style detectors are widely used due to their strong performance, they often struggle to accurately localize edge-dominant defects such...
Multi-Task and Federated Learning for Breast and Lung Cancer Screening and Diagnosis: A Survey and Future Research Directions [0.03%]
乳腺癌和肺癌的多任务和联邦学习筛查与诊断：综述与未来研究方向
Alexandru Ciobotaru,Cosmina Corches,Dan Gota et al.
Alexandru Ciobotaru et al.
Background: Breast cancer (BrC) and lung cancer (LuC) are two forms of aggressive cancer that affect both men and women worldwide. Recently, multitask learning (MTL) and federated learning (FL) techniques have proven to b...
A Generalized Deep Learning Pipeline for Stain-Invariant Ultrastructural Segmentation in Peripheral Nerves [0.03%]
一种通用的深度学习管线，用于外周神经组织切片不变性的超微结构分割
Vitalijs Borisovs,Guido Cavaletti
Vitalijs Borisovs
Automated analysis of peripheral nerve ultrastructure is bottlenecked by heterogeneous electron microscopy (EM) datasets, where varying staining protocols and resolutions create domain shifts that confound deep learning. To address this, we...
Structure-Guided Tooth Numbering and Lesion Localization in Visible Light Oral Images [0.03%]
基于结构的牙齿编号和可见光口腔图像中的病变定位
Yuhuang Lin,Youcheng Luo,Fengzhen Gao et al.
Yuhuang Lin et al.
This study presents a structure-aware inference framework for tooth numbering and lesion localization in visible light oral images. Tooth numbering is often compromised by class imbalance and structural inconsistency caused by the uneven di...
A Multimodal Dense Parallel Global Attention Mechanism for Brain Tumor Image Segmentation [0.03%]
一种多模态密集并行全局注意力机制的脑肿瘤图像分割方法
Zhuye Xu,Ru Qiao
Zhuye Xu
Brain tumor segmentation from 3D MRI presents significant challenges due to small lesion sizes, ambiguous boundaries, arbitrary spatial distributions, and heterogeneous morphological properties. To tackle these issues, this paper presents a...