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Found 23 papers, 11 papers with code
Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retinal OCT Images with Full and Sparse Annotations
Experiments on a synthetic dataset and three public clinical datasets show that our framework can effectively align the B-scans for potential motion correction, and achieves superior performance to state-of-the-art 2D deep learning methods in terms of both layer segmentation accuracy and cross-B-scan 3D continuity in both fully and semi-supervised settings, thus offering more clinical values than previous works.
Automatic view plane prescription for cardiac magnetic resonance imaging via supervision by spatial relationship between views
Then, a multi-view planning strategy is proposed to aggregate information from the predicted heatmaps for all the source views of a target plane, for a globally optimal prescription, mimicking the similar strategy practiced by skilled human prescribers.
You've Got Two Teachers: Co-evolutionary Image and Report Distillation for Semi-supervised Anatomical Abnormality Detection in Chest X-ray
Inversely, we also use the prediction of the vision detection model for abnormality-guided pseudo classification label refinement (APCLR) in the auxiliary report classification task, and propose a co-evolution strategy where the vision and report models mutually promote each other with RPDLR and APCLR performed alternatively.
M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing Modalities
In the first stage, a multimodal masked autoencoder (M3AE) is proposed, where both random modalities (i. e., modality dropout) and random patches of the remaining modalities are masked for a reconstruction task, for self-supervised learning of robust multimodal representations against missing modalities.
RECIST Weakly Supervised Lesion Segmentation via Label-Space Co-Training
As an essential indicator for cancer progression and treatment response, tumor size is often measured following the response evaluation criteria in solid tumors (RECIST) guideline in CT slices.
MADAv2: Advanced Multi-Anchor Based Active Domain Adaptation Segmentation
Unsupervised domain adaption has been widely adopted in tasks with scarce annotated data.
Segmentation-guided Domain Adaptation and Data Harmonization of Multi-device Retinal Optical Coherence Tomography using Cycle-Consistent Generative Adversarial Networks
Optical Coherence Tomography(OCT) is a non-invasive technique capturing cross-sectional area of the retina in micro-meter resolutions.
Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation
A key to this challenging task is to fully utilize the information in the support images by exploiting fine-grained correlations between the query and support images.
Ranked #4 on
Few-Shot Semantic Segmentation
on COCO-20i (1-shot)
Deformer: Towards Displacement Field Learning for Unsupervised Medical Image Registration
In this study, we propose a novel Deformer module along with a multi-scale framework for the deformable image registration task.
Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retina OCT Images
To the best of our knowledge, this is the first study that attempts 3D retinal layer segmentation in volumetric OCT images based on CNNs.
All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation
Observing this, we ask an unexplored but interesting question: can we exploit the unlabeled data via explicit real label supervision for semi-supervised training?
Differential Diagnosis of Frontotemporal Dementia and Alzheimer's Disease using Generative Adversarial Network
Frontotemporal dementia and Alzheimer's disease are two common forms of dementia and are easily misdiagnosed as each other due to their similar pattern of clinical symptoms.
Multi-Anchor Active Domain Adaptation for Semantic Segmentation
Unsupervised domain adaption has proven to be an effective approach for alleviating the intensive workload of manual annotation by aligning the synthetic source-domain data and the real-world target-domain samples.
Double-Uncertainty Guided Spatial and Temporal Consistency Regularization Weighting for Learning-based Abdominal Registration
Such convention has two limitations: (i) Besides the laborious grid search for the optimal fixed weight, the regularization strength of a specific image pair should be associated with the content of the images, thus the "one value fits all" training scheme is not ideal; (ii) Only spatially regularizing the transformation may neglect some informative clues related to the ill-posedness.
Noisy Labels are Treasure: Mean-Teacher-Assisted Confident Learning for Hepatic Vessel Segmentation
Manually segmenting the hepatic vessels from Computer Tomography (CT) is far more expertise-demanding and laborious than other structures due to the low-contrast and complex morphology of vessels, resulting in the extreme lack of high-quality labeled data.
Deep Image Clustering with Category-Style Representation
In this paper, we propose a novel deep image clustering framework to learn a category-style latent representation in which the category information is disentangled from image style and can be directly used as the cluster assignment.
A Macro-Micro Weakly-supervised Framework for AS-OCT Tissue Segmentation
Primary angle closure glaucoma (PACG) is the leading cause of irreversible blindness among Asian people.
Quality Control of Neuron Reconstruction Based on Deep Learning
To ensure the quality of reconstructed neurons and provide guidance for annotators to improve their efficiency, we propose a deep learning based quality control method for neuron reconstruction in this paper.
Cascade Dual-branch Deep Neural Networks for Retinal Layer and fluid Segmentation of Optical Coherence Tomography Incorporating Spatial Prior
Optical coherence tomography (OCT) is a non-invasive imaging technology that can provide micrometer-resolution cross-sectional images of the inner structures of the eye.
Cascaded Deep Neural Networks for Retinal Layer Segmentation of Optical Coherence Tomography with Fluid Presence
Optical coherence tomography (OCT) is a non-invasive imaging technology which can provide micrometer-resolution cross-sectional images of the inner structures of the eye.
Development and validation of a novel dementia of Alzheimer's type (DAT) score based on metabolism FDG-PET imaging
Fluorodeoxyglucose positron emission tomography (FDG-PET) imaging based 3D topographic brain glucose metabolism patterns from normal controls (NC) and individuals with dementia of Alzheimer's type (DAT) are used to train a novel multi-scale ensemble classification model.
Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer's Disease using structural MR and FDG-PET images
Alzheimer's Disease (AD) is a progressive neurodegenerative disease.
Retinal Fluid Segmentation and Detection in Optical Coherence Tomography Images using Fully Convolutional Neural Network
As a non-invasive imaging modality, optical coherence tomography (OCT) can provide micrometer-resolution 3D images of retinal structures.
