MLDRL: Multi-loss disentangled representation learning for predicting esophageal cancer response to neoadjuvant chemoradiotherapy using longitudinal CT images.
Sign Up to like & getrecommendations! Published in 2022 at "Medical image analysis"
DOI: 10.1016/j.media.2022.102423
Abstract: Accurate prediction of pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) is essential for clinical precision treatment. However, the existing methods of predicting pCR in esophageal cancer are based on the single stage data, which… read more here.
Keywords: loss disentangled; disentangled representation; esophageal cancer; loss ... See more keywords
Integration of single cell data by disentangled representation learning
Sign Up to like & getrecommendations! Published in 2021 at "Nucleic Acids Research"
DOI: 10.1093/nar/gkab978
Abstract: Abstract Recent developments of single cell RNA-sequencing technologies lead to the exponential growth of single cell sequencing datasets across different conditions. Combining these datasets helps to better understand cellular identity and function. However, it is… read more here.
Keywords: integration; single cell; representation learning; disentangled representation ... See more keywords
Weakly Supervised Disentangled Representation for Goal-Conditioned Reinforcement Learning
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Robotics and Automation Letters"
DOI: 10.1109/lra.2022.3141148
Abstract: Goal-conditioned reinforcement learning is a crucial yet challenging algorithm which enables agents to achieve multiple user-specified goals when learning a set of skills in a dynamic environment. However, it typically requires millions of the environmental… read more here.
Keywords: representation; disentangled representation; conditioned reinforcement; reinforcement learning ... See more keywords
DCDR-GAN: A Densely Connected Disentangled Representation Generative Adversarial Network for Infrared and Visible Image Fusion
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Circuits and Systems for Video Technology"
DOI: 10.1109/tcsvt.2022.3206807
Abstract: This paper proposes a new infrared and visible image fusion method based on the densely connected disentangled representation generative adversarial network (DCDR-GAN), which strips the content and the modal features of infrared and visible images… read more here.
Keywords: fusion; disentangled representation; densely connected; dcdr gan ... See more keywords
Learning Disentangled Representation for Mixed- Reality Human Activity Recognition With a Single IMU Sensor
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Instrumentation and Measurement"
DOI: 10.1109/tim.2021.3111996
Abstract: Together with the rapid development of the sensors technology in recent years, sensor-based human activity recognition (HAR) has shown promising performance using well-known supervised deep learning methods. However, it remains challenging in a realistic scenario,… read more here.
Keywords: disentangled representation; human activity; imu sensor; activity recognition ... See more keywords
Disentangled Representation for Cross-Domain Medical Image Segmentation
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Instrumentation and Measurement"
DOI: 10.1109/tim.2022.3221131
Abstract: Image segmentation is a long-standing problem in medical image analysis to facilitate the clinical diagnosis and intervention. Progress has been made due to deep learning via supervised training with elaborate human labeling, and however, the… read more here.
Keywords: disentangled representation; image segmentation; domain; medical image ... See more keywords
Understanding and Modeling Urban Mobility Dynamics via Disentangled Representation Learning
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Intelligent Transportation Systems"
DOI: 10.1109/tits.2020.3030259
Abstract: Understanding the underlying patterns of the urban mobility dynamics is essential for both the traffic state estimation and management of urban facilities and services. Due to the coupling relationship of generative factors in spatial-temporal domain,… read more here.
Keywords: disentangled representation; generative factors; traffic; mobility dynamics ... See more keywords
Noise-Powered Disentangled Representation for Unsupervised Speckle Reduction of Optical Coherence Tomography Images
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Medical Imaging"
DOI: 10.1109/tmi.2020.3045207
Abstract: Due to its noninvasive character, optical coherence tomography (OCT) has become a popular diagnostic method in clinical settings. However, the low-coherence interferometric imaging procedure is inevitably contaminated by heavy speckle noise, which impairs both visual… read more here.
Keywords: optical coherence; speckle reduction; disentangled representation; coherence ... See more keywords
Disentangled Representation Learning for Cross-Modal Biometric Matching
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Multimedia"
DOI: 10.1109/tmm.2021.3071243
Abstract: Cross-modal biometric matching (CMBM) aims to determine the corresponding voice from a face, or identify the corresponding face from a voice. Recently, many CMBM methods have been proposed by forcing the distance between two modal… read more here.
Keywords: disentangled representation; cross modal; identity information; identity ... See more keywords
Incremental Embedding Learning With Disentangled Representation Translation.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3199816
Abstract: Humans are capable of accumulating knowledge by sequentially learning different tasks, while neural networks fail to achieve this due to catastrophic forgetting problems. Most current incremental learning methods focus more on tackling catastrophic forgetting for… read more here.
Keywords: disentangled representation; representation translation; catastrophic forgetting; embedding networks ... See more keywords