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Found 16 papers, 3 papers with code
Bundle-specific Tractogram Distribution Estimation Using Higher-order Streamline Differential Equation
At the global level, the tractography process is simplified as the estimation of bundle-specific tractogram distribution (BTD) coefficients by minimizing the energy optimization model, and is used to characterize the relations between BTD and diffusion tensor vector under the prior guidance by introducing the tractogram bundle information to provide anatomic priors.
Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods
The corticospinal tract (CST) is a critically important white matter fiber tract in the human brain that enables control of voluntary movements of the body.
DeepRGVP: A Novel Microstructure-Informed Supervised Contrastive Learning Framework for Automated Identification Of The Retinogeniculate Pathway Using dMRI Tractography
The retinogeniculate pathway (RGVP) is responsible for carrying visual information from the retina to the lateral geniculate nucleus.
Bayesian Evidential Learning for Few-Shot Classification
Few-Shot Classification(FSC) aims to generalize from base classes to novel classes given very limited labeled samples, which is an important step on the path toward human-like machine learning.
White Matter Tracts are Point Clouds: Neuropsychological Score Prediction and Critical Region Localization via Geometric Deep Learning
In this paper, we propose a deep-learning-based framework for neuropsychological score prediction using microstructure measurements estimated from diffusion magnetic resonance imaging (dMRI) tractography, focusing on predicting performance on a receptive vocabulary assessment task based on a critical fiber tract for language, the arcuate fasciculus (AF).
Memory-Based Label-Text Tuning for Few-Shot Class-Incremental Learning
The difficulties are that training on a sequence of limited data from new tasks leads to severe overfitting issues and causes the well-known catastrophic forgetting problem.
Switchable Representation Learning Framework with Self-compatibility
Existing research mainly focuses on the one-to-one compatible paradigm, which is limited in learning compatibility among multiple models.
Geometric Anchor Correspondence Mining With Uncertainty Modeling for Universal Domain Adaptation
Therefore, in this paper, we propose a Geometric anchor-guided Adversarial and conTrastive learning framework with uncErtainty modeling called GATE to alleviate these issues.
Ranked #5 on
Universal Domain Adaptation
on Office-Home
T-SVDNet: Exploring High-Order Prototypical Correlations for Multi-Source Domain Adaptation
Most existing domain adaptation methods focus on adaptation from only one source domain, however, in practice there are a number of relevant sources that could be leveraged to help improve performance on target domain.
Ranked #2 on
Unsupervised Domain Adaptation
on PACS
ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Image Segmentation
Semi-supervised learning is a useful tool for image segmentation, mainly due to its ability in extracting knowledge from unlabeled data to assist learning from labeled data.
Multi-Target Domain Adaptation with Collaborative Consistency Learning
To obtain a single model that works across multiple target domains, we propose to simultaneously learn a student model which is trained to not only imitate the output of each expert on the corresponding target domain, but also to pull different expert close to each other with regularization on their weights.
Ranked #4 on
Domain Adaptation
on GTAV to Cityscapes+Mapillary
Multi-Source Domain Adaptation with Collaborative Learning for Semantic Segmentation
Multi-source unsupervised domain adaptation~(MSDA) aims at adapting models trained on multiple labeled source domains to an unlabeled target domain.
Ranked #1 on
Domain Adaptation
on GTA5+Synscapes to Cityscapes
Multi-Source Unsupervised Domain Adaptation
Semantic Segmentation
+1
Semi-supervised Domain Adaptation based on Dual-level Domain Mixing for Semantic Segmentation
To address the task of SSDA, a novel framework based on dual-level domain mixing is proposed.
Can Semantic Labels Assist Self-Supervised Visual Representation Learning?
Recently, contrastive learning has largely advanced the progress of unsupervised visual representation learning.
ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Medical Image Segmentation
This paper focuses on a popular pipeline known as self learning, and points out a weakness named lazy learning that refers to the difficulty for a model to learn from the pseudo labels generated by itself.
Bi-Directional Cascade Network for Perceptual Edge Detection
Exploiting multi-scale representations is critical to improve edge detection for objects at different scales.
Ranked #2 on
Edge Detection
on BRIND
