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Found 34 papers, 1 papers with code
Free-Mask: A Novel Paradigm of Integration Between the Segmentation Diffusion Model and Image Editing to Improve Segmentation Ability
Current semantic segmentation models typically require a substantial amount of manually annotated data, a process that is both time-consuming and resource-intensive.
Semi-Supervised Bone Marrow Lesion Detection from Knee MRI Segmentation Using Mask Inpainting Models
The proposed method has shown a potential in improving BML detection, laying a foundation for further advances in imaging-based OA research.
Label-Efficient 3D Brain Segmentation via Complementary 2D Diffusion Models with Orthogonal Views
Deep learning-based segmentation techniques have shown remarkable performance in brain segmentation, yet their success hinges on the availability of extensive labeled training data.
Speech motion anomaly detection via cross-modal translation of 4D motion fields from tagged MRI
In this work, we aim to develop a framework for detecting speech motion anomalies in conjunction with their corresponding speech acoustics.
Disentangled Multimodal Brain MR Image Translation via Transformer-based Modality Infuser
Multimodal Magnetic Resonance (MR) Imaging plays a crucial role in disease diagnosis due to its ability to provide complementary information by analyzing a relationship between multimodal images on the same subject.
Speech Audio Synthesis from Tagged MRI and Non-Negative Matrix Factorization via Plastic Transformer
The tongue's intricate 3D structure, comprising localized functional units, plays a crucial role in the production of speech.
MomentaMorph: Unsupervised Spatial-Temporal Registration with Momenta, Shooting, and Correction
We introduce a novel "momenta, shooting, and correction" framework for Lagrangian motion estimation in the presence of repetitive patterns and large motion.
Incremental Learning for Heterogeneous Structure Segmentation in Brain Tumor MRI
Deep learning (DL) models for segmenting various anatomical structures have achieved great success via a static DL model that is trained in a single source domain.
Attentive Continuous Generative Self-training for Unsupervised Domain Adaptive Medical Image Translation
We evaluated our framework on two cross-scanner/center, inter-subject translation tasks, including tagged-to-cine magnetic resonance (MR) image translation and T1-weighted MR-to-fractional anisotropy translation.
Synthesizing audio from tongue motion during speech using tagged MRI via transformer
However, elucidating the relationship between these two sources of information is challenging, due in part to the disparity in data structure between spatiotemporal motion fields (i. e., 4D motion fields) and one-dimensional audio waveforms.
Successive Subspace Learning for Cardiac Disease Classification with Two-phase Deformation Fields from Cine MRI
Cardiac cine magnetic resonance imaging (MRI) has been used to characterize cardiovascular diseases (CVD), often providing a noninvasive phenotyping tool.~While recently flourished deep learning based approaches using cine MRI yield accurate characterization results, the performance is often degraded by small training samples.
DRIMET: Deep Registration for 3D Incompressible Motion Estimation in Tagged-MRI with Application to the Tongue
We show that the method outperforms existing approaches, and also exhibits improvements in speed, robustness to tag fading, and large tongue motion.
Memory Consistent Unsupervised Off-the-Shelf Model Adaptation for Source-Relaxed Medical Image Segmentation
Unsupervised domain adaptation (UDA) has been a vital protocol for migrating information learned from a labeled source domain to facilitate the implementation in an unlabeled heterogeneous target domain.
Subtype-Aware Dynamic Unsupervised Domain Adaptation
In TPN, while the closeness of class centers between source and target domains is explicitly enforced in a latent space, the underlying fine-grained subtype structure and the cross-domain within-class compactness have not been fully investigated.
Unsupervised Domain Adaptation for Segmentation with Black-box Source Model
Unsupervised domain adaptation (UDA) has been widely used to transfer knowledge from a labeled source domain to an unlabeled target domain to counter the difficulty of labeling in a new domain.
Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives
Unsupervised domain adaptation (UDA) is proposed to counter this, by leveraging both labeled source domain data and unlabeled target domain data to carry out various tasks in the target domain.
Tagged-MRI Sequence to Audio Synthesis via Self Residual Attention Guided Heterogeneous Translator
Understanding the underlying relationship between tongue and oropharyngeal muscle deformation seen in tagged-MRI and intelligible speech plays an important role in advancing speech motor control theories and treatment of speech related-disorders.
ACT: Semi-supervised Domain-adaptive Medical Image Segmentation with Asymmetric Co-training
Unsupervised domain adaptation (UDA) has been vastly explored to alleviate domain shifts between source and target domains, by applying a well-performed model in an unlabeled target domain via supervision of a labeled source domain.
Structure-aware Unsupervised Tagged-to-Cine MRI Synthesis with Self Disentanglement
Specifically, we propose a novel input-output image patches self-training scheme to achieve a disentanglement of underlying anatomical structures and imaging modalities.
Variational Inference for Quantifying Inter-observer Variability in Segmentation of Anatomical Structures
Then, we apply a variational autoencoder network and optimize its evidence lower bound (ELBO) to efficiently approximate the distribution of the segmentation map, given an MR image.
Self-semantic contour adaptation for cross modality brain tumor segmentation
Unsupervised domain adaptation (UDA) between two significantly disparate domains to learn high-level semantic alignment is a crucial yet challenging task.~To this end, in this work, we propose exploiting low-level edge information to facilitate the adaptation as a precursor task, which has a small cross-domain gap, compared with semantic segmentation.~The precise contour then provides spatial information to guide the semantic adaptation.
Adversarial Unsupervised Domain Adaptation with Conditional and Label Shift: Infer, Align and Iterate
In this work, we propose an adversarial unsupervised domain adaptation (UDA) approach with the inherent conditional and label shifts, in which we aim to align the distributions w. r. t.
Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference
In this work, we propose a domain generalization (DG) approach to learn on several labeled source domains and transfer knowledge to a target domain that is inaccessible in training.
Segmentation of Cardiac Structures via Successive Subspace Learning with Saab Transform from Cine MRI
Assessment of cardiovascular disease (CVD) with cine magnetic resonance imaging (MRI) has been used to non-invasively evaluate detailed cardiac structure and function.
Generative Self-training for Cross-domain Unsupervised Tagged-to-Cine MRI Synthesis
Self-training based unsupervised domain adaptation (UDA) has shown great potential to address the problem of domain shift, when applying a trained deep learning model in a source domain to unlabeled target domains.
Adapting Off-the-Shelf Source Segmenter for Target Medical Image Segmentation
To alleviate this, in this work, we target source free UDA for segmentation, and propose to adapt an ``off-the-shelf" segmentation model pre-trained in the source domain to the target domain, with an adaptive batch-wise normalization statistics adaptation framework.
Symmetric-Constrained Irregular Structure Inpainting for Brain MRI Registration with Tumor Pathology
Deformable registration of magnetic resonance images between patients with brain tumors and healthy subjects has been an important tool to specify tumor geometry through location alignment and facilitate pathological analysis.
A Unified Conditional Disentanglement Framework for Multimodal Brain MR Image Translation
Our framework hinges on a cycle-constrained conditional adversarial training approach, where it can extract a modality-invariant anatomical feature with a modality-agnostic encoder and generate a target modality with a conditioned decoder.
Dual-cycle Constrained Bijective VAE-GAN For Tagged-to-Cine Magnetic Resonance Image Synthesis
Tagged magnetic resonance imaging (MRI) is a widely used imaging technique for measuring tissue deformation in moving organs.
VoxelHop: Successive Subspace Learning for ALS Disease Classification Using Structural MRI
Deep learning has great potential for accurate detection and classification of diseases with medical imaging data, but the performance is often limited by the number of training datasets and memory requirements.
Subtype-aware Unsupervised Domain Adaptation for Medical Diagnosis
Recent advances in unsupervised domain adaptation (UDA) show that transferable prototypical learning presents a powerful means for class conditional alignment, which encourages the closeness of cross-domain class centroids.
A Deep Joint Sparse Non-negative Matrix Factorization Framework for Identifying the Common and Subject-specific Functional Units of Tongue Motion During Speech
Experiments carried out with in vivo tongue motion data show that the proposed method can determine the common and subject-specific functional units with increased interpretability and decreased size variability.
A Sparse Non-negative Matrix Factorization Framework for Identifying Functional Units of Tongue Behavior from MRI
We then use three-dimensional synthetic and \textit{in vivo} tongue motion data using protrusion and simple speech tasks to identify subject-specific and data-driven functional units of the tongue in localized regions.
Speech Map: A Statistical Multimodal Atlas of 4D Tongue Motion During Speech from Tagged and Cine MR Images
Quantitative measurement of functional and anatomical traits of 4D tongue motion in the course of speech or other lingual behaviors remains a major challenge in scientific research and clinical applications.
