Search Results for author:
Found 39 papers, 2 papers with code
MambaMIR: An Arbitrary-Masked Mamba for Joint Medical Image Reconstruction and Uncertainty Estimation
The recent Mamba model has shown remarkable adaptability for visual representation learning, including in medical imaging tasks.
JCCS-PFGM: A Novel Circle-Supervision based Poisson Flow Generative Model for Multiphase CECT Progressive Low-Dose Reconstruction with Joint Condition
Multiphase contrast-enhanced computed tomography (CECT) scan is clinically significant to demonstrate the anatomy at different phases.
Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification
Failure to recognize samples from the classes unseen during training is a major limitation of artificial intelligence in the real-world implementation for recognition and classification of retinal anomalies.
Model Barrier: A Compact Un-Transferable Isolation Domain for Model Intellectual Property Protection
As scientific and technological advancements result from human intellectual labor and computational costs, protecting model intellectual property (IP) has become increasingly important to encourage model creators and owners.
sMRI-PatchNet: A novel explainable patch-based deep learning network for Alzheimer's disease diagnosis and discriminative atrophy localisation with Structural MRI
Specifically, it consists of two primary components: 1) A fast and efficient explainable patch selection mechanism for determining the most discriminative patches based on computing the SHapley Additive exPlanations (SHAP) contribution to a transfer learning model for AD diagnosis on massive medical data; and 2) A novel patch-based network for extracting deep features and AD classfication from the selected patches with position embeddings to retain position information, capable of capturing the global and local information of inter- and intra-patches.
Complementary Labels Learning with Augmented Classes
In this paper, we propose a novel problem setting called Complementary Labels Learning with Augmented Classes (CLLAC), which brings the challenge that classifiers trained by complementary labels should not only be able to classify the instances from observed classes accurately, but also recognize the instance from the Augmented Classes in the testing phase.
Unsupervised Domain Adaptation via Style-Aware Self-intermediate Domain
Specifically, we propose a novel learning strategy of SSID, which selects samples from both source and target domains as anchors, and then randomly fuses the object and style features of these anchors to generate labeled and style-rich intermediate auxiliary features for knowledge transfer.
Learning from Positive and Unlabeled Data with Augmented Classes
In this paper, we propose an unbiased risk estimator for PU learning with Augmented Classes (PUAC) by utilizing unlabeled data from the augmented classes distribution, which can be easily collected in many real-world scenarios.
Layer-Wise Partitioning and Merging for Efficient and Scalable Deep Learning
Deep Neural Network (DNN) models are usually trained sequentially from one layer to another, which causes forward, backward and update locking's problems, leading to poor performance in terms of training time.
InfoAT: Improving Adversarial Training Using the Information Bottleneck Principle
Therefore, guaranteeing the robustness of hard examples is crucial for improving the final robustness of the model.
Low-Dose CT Denoising via Sinogram Inner-Structure Transformer
Furthermore, in order to improve the performance in the image domain, we propose the image reconstruction module to complementarily denoise both in the sinogram and image domain.
Scale-Invariant Adversarial Attack for Evaluating and Enhancing Adversarial Defenses
Further, we propose Scale-Invariant (SI) adversarial defense mechanism based on the cosine angle matrix, which can be embedded into the popular adversarial defenses.
MedRDF: A Robust and Retrain-Less Diagnostic Framework for Medical Pretrained Models Against Adversarial Attack
However, the defense methods that have good effect in natural images may not be suitable for medical diagnostic tasks.
Towards Evaluating the Robustness of Deep Diagnostic Models by Adversarial Attack
Deep learning models (with neural networks) have been widely used in challenging tasks such as computer-aided disease diagnosis based on medical images.
Improving the Certified Robustness of Neural Networks via Consistency Regularization
A range of defense methods have been proposed to improve the robustness of neural networks on adversarial examples, among which provable defense methods have been demonstrated to be effective to train neural networks that are certifiably robust to the attacker.
Transport based Graph Kernels
In order to effectively utilize graph hierarchical structure information, we propose pyramid graph kernel based on optimal transport (OT).
Shared Space Transfer Learning for analyzing multi-site fMRI data
The optimization procedure extracts the common features for each site by using a single-iteration algorithm and maps these site-specific common features to the site-independent shared space.
ASMFS: Adaptive-Similarity-based Multi-modality Feature Selection for Classification of Alzheimer's Disease
With the increasing amounts of high-dimensional heterogeneous data to be processed, multi-modality feature selection has become an important research direction in medical image analysis.
Deep Representational Similarity Learning for analyzing neural signatures in task-based fMRI dataset
This paper develops Deep Representational Similarity Learning (DRSL), a deep extension of RSA that is appropriate for analyzing similarities between various cognitive tasks in fMRI datasets with a large number of subjects, and high-dimensionality -- such as whole-brain images.
Ordinal Pattern Kernel for Brain Connectivity Network Classification
Kernel-based method, such as graph kernel (i. e., kernel defined on graphs), has been proposed for measuring the similarity of brain networks, and yields the promising classification performance.
An Explainable 3D Residual Self-Attention Deep Neural Network FOR Joint Atrophy Localization and Alzheimer's Disease Diagnosis using Structural MRI
Different from the existing approaches, the novelty of our approach is three-fold: 1) A Residual Self-Attention Deep Neural Network has been proposed to capture local, global and spatial information of MR images to improve diagnostic performance; 2) An explanation method using Gradient-based Localization Class Activation mapping (Grad-CAM) has been introduced to improve the explainable of the proposed method; 3) This work has provided a full end-to-end learning solution for automated disease diagnosis.
A Survey on Deep Learning for Neuroimaging-based Brain Disorder Analysis
Deep learning has been recently used for the analysis of neuroimages, such as structural magnetic resonance imaging (MRI), functional MRI, and positron emission tomography (PET), and has achieved significant performance improvements over traditional machine learning in computer-aided diagnosis of brain disorders.
Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification with Chest CT
We evaluated our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP).
Geometric Interpretation of Running Nyström-Based Kernel Machines and Error Analysis
These analytical results lead to the conjecture that the naive approach can provide more accurate approximate solutions than the other two sophisticated approaches.
Supervised Hyperalignment for multi-subject fMRI data alignment
This paper proposes a Supervised Hyperalignment (SHA) method to ensure better functional alignment for MVP analysis, where the proposed method provides a supervised shared space that can maximize the correlation among the stimuli belonging to the same category and minimize the correlation between distinct categories of stimuli.
Graph-Based Decoding Model for Functional Alignment of Unaligned fMRI Data
Aggregating multi-subject functional magnetic resonance imaging (fMRI) data is indispensable for generating valid and general inferences from patterns distributed across human brains.
SEGAN: Structure-Enhanced Generative Adversarial Network for Compressed Sensing MRI Reconstruction
Generative Adversarial Networks (GANs) are powerful tools for reconstructing Compressed Sensing Magnetic Resonance Imaging (CS-MRI).
Gradient-based Representational Similarity Analysis with Searchlight for Analyzing fMRI Data
Classical RSA techniques employ the inverse of the covariance matrix to explore a linear model between the neural activities and task events.
Multi-Objective Cognitive Model: a supervised approach for multi-subject fMRI analysis
As a standard pipeline in the MVP analysis, brain patterns in multi-subject fMRI dataset must be mapped to a shared space and then a classification model is generated by employing the mapped patterns.
Gradient Hyperalignment for multi-subject fMRI data alignment
Multi-subject fMRI data analysis is an interesting and challenging problem in human brain decoding studies.
Deep Hyperalignment
This paper proposes Deep Hyperalignment (DHA) as a regularized, deep extension, scalable Hyperalignment (HA) method, which is well-suited for applying functional alignment to fMRI datasets with nonlinearity, high-dimensionality (broad ROI), and a large number of subjects.
Anatomical Pattern Analysis for decoding visual stimuli in human brains
Methods: In overcoming mentioned challenges, this paper proposes Anatomical Pattern Analysis (APA) for decoding visual stimuli in the human brain.
Multi-Region Neural Representation: A novel model for decoding visual stimuli in human brains
There is a wide range of challenges in the MVP techniques, i. e. decreasing noise and sparsity, defining effective regions of interest (ROIs), visualizing results, and the cost of brain studies.
WoCE: a framework for clustering ensemble by exploiting the wisdom of Crowds theory
We employ four conditions in the WOC theory, i. e., diversity, independency, decentralization and aggregation, to guide both the constructing of individual clustering results and the final combination for clustering ensemble.
Local Discriminant Hyperalignment for multi-subject fMRI data alignment
Multivariate Pattern (MVP) classification can map different cognitive states to the brain tasks.
A new selection strategy for selective cluster ensemble based on Diversity and Independency
In recent years, Diversity and Quality, which are two metrics in evaluation procedure, have been used for selecting basic clustering results in the cluster ensemble selection.
Decoding visual stimuli in human brain by using Anatomical Pattern Analysis on fMRI images
In overcoming mentioned challenges, this paper proposes Anatomical Pattern Analysis (APA) for decoding visual stimuli in the human brain.
Weighted Spectral Cluster Ensemble
Cluster Ensemble Selection (CES) is a new approach, which can combine individual clustering results for increasing the performance of the final results.
Prostate Segmentation in CT Images via Spatial-Constrained Transductive Lasso
Specifically, to segment the prostate in the current treatment image, the physician first takes a few seconds to manually specify the first and last slices of the prostate in the image space.
