Search Results for author:
Found 17 papers, 9 papers with code
A Clinical-oriented Multi-level Contrastive Learning Method for Disease Diagnosis in Low-quality Medical Images
Nevertheless, the effectiveness of CL is highly dependent on the quality of the positive and negative sample pairs.
HRLAIF: Improvements in Helpfulness and Harmlessness in Open-domain Reinforcement Learning From AI Feedback
Using ChatGPT as a labeler to provide feedback on open-domain prompts in RLAIF training, we observe an increase in human evaluators' preference win ratio for model responses, but a decrease in evaluators' satisfaction rate.
Narrowing the semantic gaps in U-Net with learnable skip connections: The case of medical image segmentation
Hence, both modules establish a learnable connection to solve the semantic gaps between the encoder and the decoder, which leads to a high-performance segmentation model for medical images.
Self-supervised Domain Adaptation for Breaking the Limits of Low-quality Fundus Image Quality Enhancement
Most of the existing image enhancement methods mainly focus on improving the image quality by leveraging the guidance of high-quality images, which is difficult to be collected in medical applications.
Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation
Combining the strengths of UMIX with CMT, UCMT can retain model disagreement and enhance the quality of pseudo labels for the co-training segmentation.
Edge Enhanced Image Style Transfer via Transformers
In recent years, arbitrary image style transfer has attracted more and more attention.
Contactless Oxygen Monitoring with Gated Transformer
With the increasing popularity of telehealth, it becomes critical to ensure that basic physiological signals can be monitored accurately at home, with minimal patient overhead.
Generalized Deep Learning-based Proximal Gradient Descent for MR Reconstruction
The data consistency for the physical forward model is crucial in inverse problems, especially in MR imaging reconstruction.
Targeted Supervised Contrastive Learning for Long-Tailed Recognition
This forces all classes, including minority classes, to maintain a uniform distribution in the feature space, improves class boundaries, and provides better generalization even in the presence of long-tail data.
Ranked #22 on
Long-tail Learning
on CIFAR-10-LT (ρ=100)
UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer
Specifically, the CTrans module is an alternate of the U-Net skip connections, which consists of a sub-module to conduct the multi-scale Channel Cross fusion with Transformer (named CCT) and a sub-module Channel-wise Cross-Attention (named CCA) to guide the fused multi-scale channel-wise information to effectively connect to the decoder features for eliminating the ambiguity.
Ranked #2 on
Medical Image Segmentation
on GlaS
(IoU metric)
Deep Learning based Spectral CT Imaging
To address the image deblurring problem associated with the $L_2^2$-loss, we propose a general $L_p^p$-loss, $p>0$ Furthermore, the images from different energy bins share similar structures of the same object, the regularization characterizing correlations of different energy bins is incorporated into the $L_p^p$-loss function, which helps unify the deep learning based methods with traditional compressed sensing based methods.
TCGM: An Information-Theoretic Framework for Semi-Supervised Multi-Modality Learning
In this paper, we propose a novel information-theoretic approach, namely \textbf{T}otal \textbf{C}orrelation \textbf{G}ain \textbf{M}aximization (TCGM), for semi-supervised multi-modal learning, which is endowed with promising properties: (i) it can utilize effectively the information across different modalities of unlabeled data points to facilitate training classifiers of each modality (ii) it has theoretical guarantee to identify Bayesian classifiers, i. e., the ground truth posteriors of all modalities.
A Spatio-temporal Transformer for 3D Human Motion Prediction
We propose a novel Transformer-based architecture for the task of generative modelling of 3D human motion.
L_DMI: A Novel Information-theoretic Loss Function for Training Deep Nets Robust to Label Noise
To the best of our knowledge, L_DMI is the first loss function that is provably robust to instance-independent label noise, regardless of noise pattern, and it can be applied to any existing classification neural networks straightforwardly without any auxiliary information.
Ranked #36 on
Image Classification
on Clothing1M
(using extra training data)
L_DMI: An Information-theoretic Noise-robust Loss Function
\emph{To the best of our knowledge, $\mathcal{L}_{DMI}$ is the first loss function that is provably robust to instance-independent label noise, regardless of noise pattern, and it can be applied to any existing classification neural networks straightforwardly without any auxiliary information}.
Ranked #36 on
Image Classification
on Clothing1M
MRI Reconstruction Using Deep Bayesian Estimation
Purpose: To develop a deep learning-based Bayesian inference for MRI reconstruction.
Max-MIG: an Information Theoretic Approach for Joint Learning from Crowds
Furthermore, we devise an accurate data-crowds forecaster that employs both the data and the crowdsourced labels to forecast the ground truth.
