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Works24 from public data
- A Multi-Organ Nucleus Segmentation Challenge558
Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics as well as heavy data augmentation in the MoNuSeg 2018 challenge.
- Learning to map 2D ultrasound images into 3D space with minimal human annotation58
A convolutional neural network is proposed that predicts the position of 2D ultrasound fetal brain scans in 3D atlas space by sampling 2D slices from 3D fetal brain volumes, and target the model to predict the inverse of the sampling process, resembling the idea of self-supervised learning.
- Sensorless volumetric reconstruction of fetal brain freehand ultrasound scans with deep implicit representation31
3D volumes reconstructed by ImplicitVol show significantly better visual and semantic quality than the existing interpolation-based reconstruction approaches, while providing richer information for diagnosis and evaluation of the developing brain.
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- Deep Fourier-Embedded Network for RGB and Thermal Salient Object Detection19
This work proposes a purely Fourier Transform-based model, namely Deep Fourier-Embedded Network (FreqSal), for accurate RGB-T SOD, and proposes Co-focus Frequency Loss, which dynamically weights hard frequencies during edge frequency reconstruction by cross-referencing bimodal edge information in the Fourier domain.
- RapidVol: Rapid Reconstruction of 3D Ultrasound Volumes from Sensorless 2D Scans16
This work proposes RapidVol: a neural representation framework to speed up slice-to-volume US reconstruction, and demonstrates that further speed-up is achievable by reconstructing from a structural prior rather than from random initialisation.
- Efficient Fourier Filtering Network With Contrastive Learning for AAV-Based Unaligned Bimodal Salient Object Detection16
An efficient Fourier filter network with contrastive learning (CL) that achieves both real time and accurate performance and boosting the performance of existing aligned BSOD models on AAV-based unaligned data is proposed.
- Adaptive 3D Localization of 2D Freehand Ultrasound Brain Images10
AdLocUI, a framework that Adaptively Localizes 2D Ultrasound Images in the 3D anatomical atlas without using any external tracking sensor, is proposed and can be used for sensorless 2D freehand ultrasound guidance by the bedside.
- Beyond benchmarks of IUGC: Rethinking requirements of deep learning method for intrapartum ultrasound biometry from fetal ultrasound videos4
Although promising results have been achieved in the Intrapartum Ultrasound Grand Challenge, the research remains in its early stages, and further in-depth exploration is required before clinical implementation, it is concluded that further in-depth exploration is required before clinical implementation.
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- FedDEAP: Adaptive Dual-Prompt Tuning for Multi-Domain Federated Learning3
An adaptive federated prompt tuning framework, FedDEAP, is proposed to enhance CLIP's generalization in multi-domain scenarios and disentangle semantic and domain-specific features in images by using semantic and domain transformation networks with unbiased mappings.
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- Unsupervised Domain Adaptation via Content Alignment for Hippocampus Segmentation2
A novel unsupervised domain adaptation framework that di-rectly addresses domain shifts encountered in cross-domain hippocampus segmentation from MRI, with specific empha-sis on content variations, which highlights the efficacy of the approach for accurate hippocampus segmentation across diverse populations.
- Bridging the Inter-Domain Gap through Low-Level Features for Cross-Modal Medical Image Segmentation2
LowBridge is proposed, which builds on the observation that cross-modal images share similar low-level features as they depict the same types of anatomical structures, and achieves state-of-the-art performance, outperforming ten existing approaches.
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- Automated synovium segmentation in doppler ultrasound images for rheumatoid arthritis assessment2
A new and robust method is proposed for automated synovium segmentation in the commonly affected joints, i.e. metacarpophalangeal (MCP) and metatarsophalangesal ( MTP) joints, which would facilitate automation in quantitative RA assessment and verify that the accuracy of segmentation by the proposed method and by clinician is comparable.
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- BoxSegUS: A Spatial Consistency Box Supervised Multi-Class Segmentation with Prior and Boundary Constraint for Ultrasound Images–
This study proposes BoxSegUS, a box-supervised framework that exploits bounding-box annotations for accurate ultrasound segmentation and enforce weak-strong spatial consistency to improve robustness against spatial variations and employ a detection-prior global context modeling mechanism to reduce the influence of unreliable local appearance cues.
- Subcortical Masks Generation in CT Images via Ensemble-Based Cross-Domain Label Transfer–
An automatic ensemble framework to generate high-quality subcortical segmentation labels for CT scans by leveraging existing MRI-based models is proposed and a robust ensembling pipeline is introduced to integrate them and apply it to unannotated paired MRI-CT data, resulting in a comprehensive CT subcortical segmentation dataset.
- OP0061 FEASIBILITY STUDY ON AN AUTOMATED QUANTITATIVE SYSTEM FOR ULTRASOUND JOINT INFLAMMATION ASSESSMENT IN RHEUMATOID ARTHRITIS USING DEEP LEARNING–
An automated quantitative system for US PD joint inflammation assessment using deep learning showed high sensitivity and specificity when results from computer prediction were compared to clinician evaluation.
- Notice of Removal: A new method for shear wave speed estimation in anisotropic tissues using wavelet transform and dynamic programming–
This study proposes dynamic programming regularized wavelet transform (DPRWT) towards robust estimation of local SWS in inhomogeneous and anisotropic soft tissues.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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