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Found 12 papers, 3 papers with code
HOPE: Hybrid-granularity Ordinal Prototype Learning for Progression Prediction of Mild Cognitive Impairment
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.
Prompt-In-Prompt Learning for Universal Image Restoration
Second, we devise a novel prompt-to-prompt interaction module to fuse these two prompts into a universal restoration prompt.
Nonlinear Multi-Carrier System with Signal Clipping: Measurement, Analysis, and Optimization
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.
FAN-Net: Fourier-Based Adaptive Normalization For Cross-Domain Stroke Lesion Segmentation
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.
CLIP-Lung: Textual Knowledge-Guided Lung Nodule Malignancy Prediction
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.
CORE: Learning Consistent Ordinal REpresentations for Image Ordinal Estimation
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.
Meta Ordinal Regression Forest for Medical Image Classification with Ordinal Labels
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.
Deep Rank-Consistent Pyramid Model for Enhanced Crowd Counting
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.
Meta ordinal weighting net for improving lung nodule classification
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.
Meta Ordinal Regression Forest For Learning with Unsure Lung Nodules
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.
PaDNet: Pan-Density Crowd Counting
We propose a novel framework, the Pan-Density Network (PaDNet), for pan-density crowd counting.
Shape and Margin-Aware Lung Nodule Classification in Low-dose CT Images via Soft Activation Mapping
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.
