1 code implementation • 19 Jan 2024 • Chenhui Wang, Yiming Lei, Tao Chen, Junping Zhang, Yuxin Li, Hongming Shan
Inspired by that various longitudinal biomarkers and cognitive measurements present an ordinal pathway on AD progression, we propose a novel Hybrid-granularity Ordinal PrototypE learning (HOPE) method to characterize AD ordinal progression for MCI progression prediction.
1 code implementation • 8 Dec 2023 • Zilong Li, Yiming Lei, Chenglong Ma, Junping Zhang, Hongming Shan
Second, we devise a novel prompt-to-prompt interaction module to fuse these two prompts into a universal restoration prompt.
no code implementations • 1 Oct 2023 • Yuyang Du, Liang Hao, Yiming Lei, Qun Yang, Shiqi Xu
With the derivations, we investigate the optimal system setting to achieve the SER lower bound in a practical OFDM system that considers both PA nonlinearity and clipping distortion.
no code implementations • 23 Apr 2023 • Weiyi Yu, Yiming Lei, Hongming Shan
To address this problem, we intend to change style information without affecting high-level semantics via adaptively changing the low-frequency amplitude components of the Fourier transform so as to enhance model robustness to varying domains.
no code implementations • 17 Apr 2023 • Yiming Lei, Zilong Li, Yan Shen, Junping Zhang, Hongming Shan
Drawing on the capability of the contrastive language-image pre-training (CLIP) model to learn generalized visual representations from text annotations, in this paper, we propose CLIP-Lung, a textual knowledge-guided framework for lung nodule malignancy prediction.
no code implementations • 15 Jan 2023 • Yiming Lei, Zilong Li, Yangyang Li, Junping Zhang, Hongming Shan
However, the manifold of the resultant feature representations does not maintain the intrinsic ordinal relations of interest, which hinders the effectiveness of the image ordinal estimation.
no code implementations • 15 Mar 2022 • Yiming Lei, Haiping Zhu, Junping Zhang, Hongming Shan
To improve model generalization with ordinal information, we propose a novel meta ordinal regression forest (MORF) method for medical image classification with ordinal labels, which learns the ordinal relationship through the combination of convolutional neural network and differential forest in a meta-learning framework.
2 code implementations • 13 Jan 2022 • Jiaqi Gao, Zhizhong Huang, Yiming Lei, Hongming Shan, James Z. Wang, Fei-Yue Wang, Junping Zhang
Specifically, we propose a Deep Rank-consistEnt pyrAmid Model (DREAM), which makes full use of rank consistency across coarse-to-fine pyramid features in latent spaces for enhanced crowd counting with massive unlabeled images.
no code implementations • 31 Jan 2021 • Yiming Lei, Hongming Shan, Junping Zhang
In this paper, we propose a Meta Ordinal Weighting Network (MOW-Net) to explicitly align each training sample with a meta ordinal set (MOS) containing a few samples from all classes.
no code implementations • 7 Dec 2020 • Yiming Lei, Haiping Zhu, Junping Zhang, Hongming Shan
Recently, an unsure data model (UDM) was proposed to incorporate those unsure nodules by formulating this problem as an ordinal regression, showing better performance over traditional binary classification.
no code implementations • 7 Nov 2018 • Yukun Tian, Yiming Lei, Junping Zhang, James Z. Wang
We propose a novel framework, the Pan-Density Network (PaDNet), for pan-density crowd counting.
no code implementations • 30 Oct 2018 • Yiming Lei, Yukun Tian, Hongming Shan, Junping Zhang, Ge Wang, Mannudeep Kalra
Therefore, CAM and Grad-CAM cannot provide optimal interpretation for lung nodule categorization task in low-dose CT images, in that fine-grained pathological clues like discrete and irregular shape and margins of nodules are capable of enhancing sensitivity and specificity of nodule classification with regards to CNN.