Search Results for author:
Found 27 papers, 10 papers with code
Novel OCT mosaicking pipeline with Feature- and Pixel-based registration
High-resolution Optical Coherence Tomography (OCT) images are crucial for ophthalmology studies but are limited by their relatively narrow field of view (FoV).
Learning Site-specific Styles for Multi-institutional Unsupervised Cross-modality Domain Adaptation
In this paper, we present our solution to tackle the multi-institutional unsupervised domain adaptation for the crossMoDA 2023 challenge.
Assessing Test-time Variability for Interactive 3D Medical Image Segmentation with Diverse Point Prompts
In this paper, we assess the test-time variability for interactive medical image segmentation with diverse point prompts.
Promise:Prompt-driven 3D Medical Image Segmentation Using Pretrained Image Foundation Models
To address prevalent issues in medical imaging, such as data acquisition challenges and label availability, transfer learning from natural to medical image domains serves as a viable strategy to produce reliable segmentation results.
False Negative/Positive Control for SAM on Noisy Medical Images
The method couples multi-box prompt augmentation and an aleatoric uncertainty-based false-negative (FN) and false-positive (FP) correction (FNPC) strategy.
CATS v2: Hybrid encoders for robust medical segmentation
We fuse the information from the convolutional encoder and the transformer at the skip connections of different resolutions to form the final segmentation.
COLosSAL: A Benchmark for Cold-start Active Learning for 3D Medical Image Segmentation
Cold-start AL is highly relevant in many practical scenarios but has been under-explored, especially for 3D medical segmentation tasks requiring substantial annotation effort.
VesselMorph: Domain-Generalized Retinal Vessel Segmentation via Shape-Aware Representation
We map the intensity image and the tensor field to a latent space for feature extraction.
Deep Angiogram: Trivializing Retinal Vessel Segmentation
The generalizability of the synthetic network is improved by the contrastive loss that makes the model less sensitive to variations of image contrast and noisy features.
COSST: Multi-organ Segmentation with Partially Labeled Datasets Using Comprehensive Supervisions and Self-training
We revisit the problem from a perspective of partial label supervision signals and identify two signals derived from ground truth and one from pseudo labels.
Self-Supervised CSF Inpainting with Synthetic Atrophy for Improved Accuracy Validation of Cortical Surface Analyses
In this paper, we present a solution using a self-supervised inpainting model to generate CSF in these regions and create images with more plausible GM/CSF boundaries.
SSL^2: Self-Supervised Learning meets Semi-Supervised Learning: Multiple Sclerosis Segmentation in 7T-MRI from large-scale 3T-MRI
A potential solution is to leverage the information available in large public datasets in conjunction with a target dataset which only has limited labeled data.
Enhancing Data Diversity for Self-training Based Unsupervised Cross-modality Vestibular Schwannoma and Cochlea Segmentation
Automatic segmentation of vestibular schwannoma (VS) and cochlea from magnetic resonance imaging can facilitate VS treatment planning.
Cats: Complementary CNN and Transformer Encoders for Segmentation
We fuse the information from the convolutional encoder and the transformer, and pass it to the decoder to obtain the results.
Segmentation of kidney stones in endoscopic video feeds
Image segmentation has been increasingly applied in medical settings as recent developments have skyrocketed the potential applications of deep learning.
ModDrop++: A Dynamic Filter Network with Intra-subject Co-training for Multiple Sclerosis Lesion Segmentation with Missing Modalities
Previously, a training strategy termed Modality Dropout (ModDrop) has been applied to MS lesion segmentation to achieve the state-of-the-art performance with missing modality.
Unsupervised Denoising of Retinal OCT with Diffusion Probabilistic Model
A diffusion process is defined by adding a sequence of Gaussian noise to self-fused OCT b-scans.
CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation
The aim was to automatically perform unilateral VS and bilateral cochlea segmentation on hrT2 as provided in the testing set (N=137).
Unsupervised Cross-Modality Domain Adaptation for Segmenting Vestibular Schwannoma and Cochlea with Data Augmentation and Model Ensemble
To overcome this problem, domain adaptation is an effective way to leverage information from source domain to obtain accurate segmentations without requiring manual labels in target domain.
LIFE: A Generalizable Autodidactic Pipeline for 3D OCT-A Vessel Segmentation
We then construct the local intensity fusion encoder (LIFE) to map a given OCT-A volume and its LIF counterpart to a shared latent space.
Retinal OCT Denoising with Pseudo-Multimodal Fusion Network
Evaluated by intensity-based and structural metrics, the result shows that our method can effectively suppress the speckle noise and enhance the contrast between retina layers while the overall structure and small blood vessels are preserved.
Multiple Sclerosis Lesion Segmentation -- A Survey of Supervised CNN-Based Methods
Lesion segmentation is a core task for quantitative analysis of MRI scans of Multiple Sclerosis patients.
Tensor-Based Grading: A Novel Patch-Based Grading Approach for the Analysis of Deformation Fields in Huntington's Disease
In this work, we propose to combine the advantages of these two approaches by extending the patch-based grading framework with a new tensor-based grading method that enables us to model patterns of local deformation using a log-Euclidean metric.
Medical Imaging with Deep Learning: MIDL 2019 -- Extended Abstract Track
This compendium gathers all the accepted extended abstracts from the Second International Conference on Medical Imaging with Deep Learning (MIDL 2019), held in London, UK, 8-10 July 2019.
Automated Segmentation of Knee MRI Using Hierarchical Classifiers and Just Enough Interaction Based Learning: Data from Osteoarthritis Initiative
We present a fully automated learning-based approach for segmenting knee cartilage in the presence of osteoarthritis (OA).
Efficient optimization for Hierarchically-structured Interacting Segments (HINTS)
We propose an effective optimization algorithm for a general hierarchical segmentation model with geometric interactions between segments.
