Research graph
References from EchoSAM2: enhancing anatomical consistency in few-shot cardiac ultrasound segmentation via sequence-wise memory and registration. Local targets link to admitted publications; unresolved targets remain external evidence.
Panet: Few-shot image semantic segmentation with prototype alignment
2019 · External reference
Self-supervised learning for few-shot medical image segmentation
10.1109/tmi.2022.3150682 · 2022 · External reference
A location-sensitive local prototype network for few-shot medical image segmentation
10.1109/isbi48211.2021.9434008 · 2021 · External reference
Rethinking few-shot medical segmentation: a vector quantization view
10.1109/cvpr52729.2023.00300 · 2023 · External reference
Video object segmentation using space-time memory networks
2019 · External reference
Rethinking space-time networks with improved memory coverage for efficient video object segmentation
10.48550/arxiv.2106.05210 · 2021 · External reference
Xmem: Long-term video object segmentation with an atkinson-shiffrin memory model
2022 · External reference
Delving deep into many-to-many attention for few-shot video object segmentation
2021 · External reference
Exploring the better correlation for few-shot video object segmentation
10.1109/tcsvt.2024.3491214 · 2024 · External reference
Customized segment anything model for medical image segmentation
10.2139/ssrn.4495221 · 2023 · External reference
Unleashing the potential of SAM for medical adaptation via hierarchical decoding
2024 · External reference
Segment anything in high quality
10.52202/075280-1303 · 2023 · External reference
Cat-sam: Conditional tuning for few-shot adaptation of segment anything model
2024 · External reference
Medical sam 2: Segment medical images as video via segment anything model 2
10.48550/arxiv.2408.00874 · 2024 · External reference
Anomaly detection-inspired few-shot medical image segmentation through self-supervision with supervoxels
10.1016/j.media.2022.102385 · 2022 · External reference
Pölsterl S, Navab N, Wachinger C. Squeeze & excite' guided few-shot segmentation of volumetric images
10.1016/j.media.2019.101587 · 2020 · External reference
Interactive few-shot learning: Limited supervision, better medical image segmentation
10.1109/tmi.2021.3060551 · 2021 · External reference
Few-shot medical image segmentation using a global correlation network with discriminative embedding
10.1016/j.compbiomed.2021.105067 · 2022 · External reference
UniverSeg: universal medical image segmentation
10.1109/iccv51070.2023.01960 · 2023 · External reference
MOSE: A new dataset for video object segmentation in complex scenes
2023 · External reference
Lvos: a benchmark for long-term video object segmentation
10.1109/iccv51070.2023.01240 · 2023 · External reference
MOVE: Motion-guided few-shot video object segmentation
2025 · External reference
Efficient video object segmentation via network modulation
2018 · External reference
Learning what to learn for video object segmentation
2020 · External reference
Sam 2: segment anything in images and videos
2025 · External reference
Segment anything
2023 · External reference
Holistic prototype attention network for few-shot video object segmentation
2023 · External reference
Temporal transductive inference for few-shot video object segmentation
10.1007/s11263-025-02390-x · 2025 · External reference
Reducing annotation burden: exploiting image knowledge for few-shot medical video object segmentation via spatiotemporal consistency relearning
2024 · External reference
regression approach for real-time 2D/3D registration
10.1109/tmi.2016.2521800 · 2016 · External reference
GMFlow: learning optical flow via global matching
10.1109/cvpr52688.2022.00795 · 2022 · External reference
Voxelmorph: a learning framework for deformable medical image registration
10.1109/tmi.2019.2897538 · 2019 · External reference
Vit-v-net: Vision transformer for unsupervised volumetric medical image registration
10.1016/j.media.2022.102615 · 2021 · External reference
CycleMorph: cycle consistent unsupervised deformable image registration
10.1016/j.media.2021.102036 · 2021 · External reference
Interpretable multi-modal image registration network based on disentangled convolutional sparse coding
10.1109/tip.2023.3240024 · 2023 · External reference
Recursive cascaded networks for unsupervised medical image registration
2019 · External reference
Affine medical image registration with coarse-to-fine vision transformer
2022 · External reference
Registration of 3D medical images based on unsupervised cooperative cascade of deep networks
10.1016/j.bspc.2023.104594 · 2023 · External reference
Recursive deformable pyramid network for unsupervised medical image registration
10.1109/tmi.2024.3362968 · 2024 · External reference
Deep learning for segmentation using an open large-scale dataset in 2D echocardiography
10.1109/tmi.2019.2900516 · 2019 · External reference
Echonet-dynamic: a large new cardiac motion video data resource for medical machine learning
2019 · External reference
Memsam: Taming segment anything model for echocardiography video segmentation
2024 · External reference
U-Net: Convolutional networks for biomedical image segmentation
2015 · External reference
Swin-UNet: UNet-like pure transformer for medical image segmentation
2022 · External reference
