Changzhe Jiao
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View article: Object Knowledge-Aware Multiple Instance Learning for Small Tumor Segmentation
Object Knowledge-Aware Multiple Instance Learning for Small Tumor Segmentation Open
View article: Diffusion-based Layer-wise Semantic Reconstruction for Unsupervised Out-of-Distribution Detection
Diffusion-based Layer-wise Semantic Reconstruction for Unsupervised Out-of-Distribution Detection Open
Unsupervised out-of-distribution (OOD) detection aims to identify out-of-domain data by learning only from unlabeled In-Distribution (ID) training samples, which is crucial for developing a safe real-world machine learning system. Current …
View article: Multi-modal fusion and feature enhancement U-Net coupling with stem cell niches proximity estimation for voxel-wise GBM recurrence prediction <sup>*</sup>
Multi-modal fusion and feature enhancement U-Net coupling with stem cell niches proximity estimation for voxel-wise GBM recurrence prediction <sup>*</sup> Open
Objective. We aim to develop a Multi-modal Fusion and Feature Enhancement U-Net (MFFE U-Net) coupling with stem cell niche proximity estimation to improve voxel-wise Glioblastoma (GBM) recurrence prediction. Approach. 57 patients with pre-…
View article: Shape-Background Joint-Aware Multiple Instance Learning for Small Tumor Segmentation
Shape-Background Joint-Aware Multiple Instance Learning for Small Tumor Segmentation Open
View article: Contrast-Enhanced Liver Magnetic Resonance Image Synthesis Using Gradient Regularized Multi-Modal Multi-Discrimination Sparse Attention Fusion GAN
Contrast-Enhanced Liver Magnetic Resonance Image Synthesis Using Gradient Regularized Multi-Modal Multi-Discrimination Sparse Attention Fusion GAN Open
Purposes: To provide abdominal contrast-enhanced MR image synthesis, we developed an gradient regularized multi-modal multi-discrimination sparse attention fusion generative adversarial network (GRMM-GAN) to avoid repeated contrast injecti…
View article: Contrast-enhanced Liver MR Synthesis using Gradient Regularized Multi-Modal Multi-Discrimination Sparse Attention Fusion GAN
Contrast-enhanced Liver MR Synthesis using Gradient Regularized Multi-Modal Multi-Discrimination Sparse Attention Fusion GAN Open
Purposes: To provide abdominal contrast-enhanced MR image synthesis, we developed an image gradient regularized multi-modal multi-discrimination sparse-attention fusion generative adversarial network (GRMM-GAN) to avoid repeated contrast i…
View article: Weakly Supervised Liver Tumor Segmentation Based on Anchor Box and Adversarial Complementary Learning
Weakly Supervised Liver Tumor Segmentation Based on Anchor Box and Adversarial Complementary Learning Open
View article: Convolutional Autoencoding and Gaussian Mixture Clustering for Unsupervised Beat-to-Beat Heart Rate Estimation of Electrocardiograms from Wearable Sensors
Convolutional Autoencoding and Gaussian Mixture Clustering for Unsupervised Beat-to-Beat Heart Rate Estimation of Electrocardiograms from Wearable Sensors Open
Heart rate is one of the most important diagnostic bases for cardiovascular disease. This paper introduces a deep autoencoding strategy into feature extraction of electrocardiogram (ECG) signals, and proposes a beat-to-beat heart rate esti…
View article: Hyperspectral Target Detection via Multiple Instance LSTM Target Localization Network
Hyperspectral Target Detection via Multiple Instance LSTM Target Localization Network Open
Modeling target detection problem given inaccurate annotations as a multiple instance learning (MIL) problem is an effective way for addressing the ground truth uncertainties of remotely sensed hyperspectral imagery. In this paper, we prop…
View article: Self-Paced Convolutional Neural Network for PolSAR Images Classification
Self-Paced Convolutional Neural Network for PolSAR Images Classification Open
Fully polarimetric synthetic aperture radar (PolSAR) can transmit and receive electromagnetic energy on four polarization channels (HH, HV, VH, VV). The data acquired from four channels have both similarities and complementarities. Utilizi…
View article: Complex Scene Classification of PoLSAR Imagery Based on a Self-Paced Learning Approach
Complex Scene Classification of PoLSAR Imagery Based on a Self-Paced Learning Approach Open
Existing polarimetric synthetic aperture radar (PolSAR) image classification\nmethods cannot achieve satisfactory performance on complex scenes characterized\nby several types of land cover with significant levels of noise or similar\nscat…
View article: Target concept learning from ambiguously labeled data
Target concept learning from ambiguously labeled data Open
The multiple instance learning problem addresses the case where training data comes with label ambiguity, i.e., the learner has access only to inaccurately labeled data. For example, in target detection from remotely sensed hyperspectral i…
View article: Multiple Instance Hybrid Estimator for Hyperspectral Target Characterization and Sub-pixel Target Detection
Multiple Instance Hybrid Estimator for Hyperspectral Target Characterization and Sub-pixel Target Detection Open
The Multiple Instance Hybrid Estimator for discriminative target characterization from imprecisely labeled hyperspectral data is presented. In many hyperspectral target detection problems, acquiring accurately labeled training data is diff…
View article: Multiple Instance Dictionary Learning for Beat-to-Beat Heart Rate Monitoring from Ballistocardiograms
Multiple Instance Dictionary Learning for Beat-to-Beat Heart Rate Monitoring from Ballistocardiograms Open
A multiple instance dictionary learning approach, Dictionary Learning using Functions of Multiple Instances (DL-FUMI), is used to perform beat-to-beat heart rate estimation and to characterize heartbeat signatures from ballistocardiogram (…
View article: Multiple Instance Hybrid Estimator for Learning Target Signatures
Multiple Instance Hybrid Estimator for Learning Target Signatures Open
Signature-based detectors for hyperspectral target detection rely on knowing the specific target signature in advance. However, target signature are often difficult or impossible to obtain. Furthermore, common methods for obtaining target …
View article: Heart beat characterization from ballistocardiogram signals using extended functions of multiple instances
Heart beat characterization from ballistocardiogram signals using extended functions of multiple instances Open
A multiple instance learning (MIL) method, extended Function of Multiple Instances (eFUMI), is applied to ballistocardiogram (BCG) signals produced by a hydraulic bed sensor. The goal of this approach is to learn a personalized heartbeat "…
View article: Multiple Instance Hyperspectral Target Characterization
Multiple Instance Hyperspectral Target Characterization Open
In this paper, two methods for multiple instance target characterization, MI-SMF and MI-ACE, are presented. MI-SMF and MI-ACE estimate a discriminative target signature from imprecisely-labeled and mixed training data. In many applications…
View article: Multiple Instance Dictionary Learning using Functions of Multiple Instances
Multiple Instance Dictionary Learning using Functions of Multiple Instances Open
A multiple instance dictionary learning method using functions of multiple instances (DL-FUMI) is proposed to address target detection and two-class classification problems with inaccurate training labels. Given inaccurate training labels,…