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Found 43 papers, 13 papers with code
An Alternative Method to Identify the Susceptibility Threshold Level of Device under Test in a Reverberation Chamber
By counting the number of pass/fail occurrences of a DUT (Device under Test) in the stirring process in a reverberation chamber (RC), the threshold electric field (E-field) level can be well estimated without tuning the input power and repeating the whole testing many times.
In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steering
On a new query, instead of adding demonstrations to the prompt, we shift the latent states of the LLM using the ICV.
3D TransUNet: Advancing Medical Image Segmentation through Vision Transformers
In this paper, we extend the 2D TransUNet architecture to a 3D network by building upon the state-of-the-art nnU-Net architecture, and fully exploring Transformers' potential in both the encoder and decoder design.
Development and external validation of a lung cancer risk estimation tool using gradient-boosting
In this study, we propose a machine learning (ML) tool trained on data from the PLCO Cancer Screening Trial and validated on the NLST to estimate the likelihood of lung cancer occurrence within five years.
Consistency-guided Meta-Learning for Bootstrapping Semi-Supervised Medical Image Segmentation
Specifically, our approach first involves training a segmentation model on a small set of clean labeled images to generate initial labels for unlabeled data.
Towards Medical Artificial General Intelligence via Knowledge-Enhanced Multimodal Pretraining
In MOTOR, we combine two kinds of basic medical knowledge, i. e., general and specific knowledge, in a complementary manner to boost the general pretraining process.
Learning Image Representations for Content Based Image Retrieval of Radiotherapy Treatment Plans
To address these limitations, we propose a content based image retrieval (CBIR) method for retrieving dose distributions of previously planned patients based on anatomical similarity.
Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging
The collection and curation of large-scale medical datasets from multiple institutions is essential for training accurate deep learning models, but privacy concerns often hinder data sharing.
GCT: Graph Co-Training for Semi-Supervised Few-Shot Learning
First, we propose a novel label prediction method, Isolated Graph Learning (IGL).
L2B: Learning to Bootstrap Robust Models for Combating Label Noise
Extensive experiments demonstrate that our method effectively mitigates the challenges of noisy labels, often necessitating few to no validation samples, and is well generalized to other tasks such as image segmentation.
Ranked #8 on
Image Classification
on Clothing1M (using clean data)
(using extra training data)
Image Classification using Graph Neural Network and Multiscale Wavelet Superpixels
On the contrary, this study investigates image classification using graphs generated from an image-specific number of multiscale superpixels.
SSDL: Self-Supervised Dictionary Learning
Inspired by the concept of self-supervised learning (e. g., setting the pretext task to generate a universal model for the downstream task), we propose a Self-Supervised Dictionary Learning (SSDL) framework to address this challenge.
MDFM: Multi-Decision Fusing Model for Few-Shot Learning
Inspired by this assumption, we propose a novel method Multi-Decision Fusing Model (MDFM), which comprehensively considers the decisions based on multiple FEMs to enhance the efficacy and robustness of the model.
RadFusion: Benchmarking Performance and Fairness for Multimodal Pulmonary Embolism Detection from CT and EHR
Despite the routine use of electronic health record (EHR) data by radiologists to contextualize clinical history and inform image interpretation, the majority of deep learning architectures for medical imaging are unimodal, i. e., they only learn features from pixel-level information.
Solving Inverse Problems in Medical Imaging with Score-Based Generative Models
These measurements are typically synthesized from images using a fixed physical model of the measurement process, which hinders the generalization capability of models to unknown measurement processes.
Metal Artifact Reduction in 2D CT Images with Self-supervised Cross-domain Learning
We then design a novel FBP reconstruction loss to encourage the network to generate more perfect completion results and a residual-learning-based image refinement module to reduce the secondary artifacts in the reconstructed CT images.
MHFC: Multi-Head Feature Collaboration for Few-Shot Learning
Apply the trained FEM to acquire the novel data's features and recognize them.
CIM: Class-Irrelevant Mapping for Few-Shot Classification
(2) Use the FEM to extract the features of novel data (with few labeled samples and totally different categories from base data), then classify them with the to-be-designed classifier.
NeRP: Implicit Neural Representation Learning with Prior Embedding for Sparsely Sampled Image Reconstruction
The method differs fundamentally from previous deep learning-based image reconstruction approaches in that NeRP exploits the internal information in an image prior, and the physics of the sparsely sampled measurements to produce a representation of the unknown subject.
A Geometry-Informed Deep Learning Framework for Ultra-Sparse 3D Tomographic Image Reconstruction
Deep learning affords enormous opportunities to augment the armamentarium of biomedical imaging, albeit its design and implementation have potential flaws.
CateNorm: Categorical Normalization for Robust Medical Image Segmentation
We propose a new normalization strategy, named categorical normalization (CateNorm), to normalize the activations according to categorical statistics.
TransCT: Dual-path Transformer for Low Dose Computed Tomography
Low dose computed tomography (LDCT) has attracted more and more attention in routine clinical diagnosis assessment, therapy planning, etc., which can reduce the dose of X-ray radiation to patients.
Learning from multiscale wavelet superpixels using GNN with spatially heterogeneous pooling
In this study, we investigate learning from a new principled representation in which individual images are represented by an image-specific number of multiscale superpixels.
Region-specific Dictionary Learning-based Low-dose Thoracic CT Reconstruction
Parameters for dictionary learning and sparse representation are determined according to the structural and noise properties of each region.
Deep Sinogram Completion with Image Prior for Metal Artifact Reduction in CT Images
Computed tomography (CT) has been widely used for medical diagnosis, assessment, and therapy planning and guidance.
Self-supervised Feature Learning via Exploiting Multi-modal Data for Retinal Disease Diagnosis
The automatic diagnosis of various retinal diseases from fundus images is important to support clinical decision-making.
Multi-Domain Image Completion for Random Missing Input Data
Multi-domain data are widely leveraged in vision applications taking advantage of complementary information from different modalities, e. g., brain tumor segmentation from multi-parametric magnetic resonance imaging (MRI).
A Mean-Field Theory for Learning the Schönberg Measure of Radial Basis Functions
In particular, we prove that in the scaling limits, the empirical measure of the Langevin particles converges to the law of a reflected It\^{o} diffusion-drift process.
Atlas Based Segmentations via Semi-Supervised Diffeomorphic Registrations
Whereas supervised segmentation methods only automate the segmentation process for a select few number of OARs, we demonstrate that our methods can achieve similar performance for OARs of interest, while also providing segmentations for every other OAR on the provided atlas.
Machine Learning Techniques for Biomedical Image Segmentation: An Overview of Technical Aspects and Introduction to State-of-Art Applications
In recent years, significant progress has been made in developing more accurate and efficient machine learning algorithms for segmentation of medical and natural images.
Modified U-Net (mU-Net) with Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images
To cope with these problems, we added a residual path with deconvolution and activation operations to the skip connection of the U-Net to avoid duplication of low resolution information of features.
A Mean-Field Theory for Kernel Alignment with Random Features in Generative Adverserial Networks
We subsequently leverage a particle stochastic gradient descent (SGD) method to solve finite dimensional optimization problems.
A Mean-Field Theory for Kernel Alignment with Random Features in Generative and Discriminative Models
We subsequently leverage a particle stochastic gradient descent (SGD) method to solve the derived finite dimensional optimization problem.
Difficulty-aware Meta-learning for Rare Disease Diagnosis
Rare diseases have extremely low-data regimes, unlike common diseases with large amount of available labeled data.
On Sample Complexity of Projection-Free Primal-Dual Methods for Learning Mixture Policies in Markov Decision Processes
We compute the actions of a policy that is nearly as good as a policy chosen by a suitable oracle from a given mixture policy class characterized by the convex hull of a set of known base policies.
Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation
In this paper, we present a novel semi-supervised method for medical image segmentation, where the network is optimized by the weighted combination of a common supervised loss for labeled inputs only and a regularization loss for both labeled and unlabeled data.
A Distributionally Robust Optimization Method for Adversarial Multiple Kernel Learning
Specifically, we consider a distributionally robust optimization of the kernel-target alignment with respect to the distribution of training samples over a distributional ball defined by the Kullback-Leibler (KL) divergence.
Quantized Minimum Error Entropy Criterion
Comparing with traditional learning criteria, such as mean square error (MSE), the minimum error entropy (MEE) criterion is superior in nonlinear and non-Gaussian signal processing and machine learning.
Deep Generative Adversarial Networks for Compressed Sensing Automates MRI
A multilayer convolutional neural network is then jointly trained based on diagnostic quality images to discriminate the projection quality.
Robustness of Maximum Correntropy Estimation Against Large Outliers
The maximum correntropy criterion (MCC) has recently been successfully applied in robust regression, classification and adaptive filtering, where the correntropy is maximized instead of minimizing the well-known mean square error (MSE) to improve the robustness with respect to outliers (or impulsive noises).
Robust Learning with Kernel Mean p-Power Error Loss
Correntropy is a second order statistical measure in kernel space, which has been successfully applied in robust learning and signal processing.
Kernel Risk-Sensitive Loss: Definition, Properties and Application to Robust Adaptive Filtering
Nonlinear similarity measures defined in kernel space, such as correntropy, can extract higher-order statistics of data and offer potentially significant performance improvement over their linear counterparts especially in non-Gaussian signal processing and machine learning.
Generalized Correntropy for Robust Adaptive Filtering
In this work, we propose a generalized correntropy that adopts the generalized Gaussian density (GGD) function as the kernel (not necessarily a Mercer kernel), and present some important properties.
