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Found 12 papers, 12 papers with code
SAMCT: Segment Any CT Allowing Labor-Free Task-Indicator Prompts
Specifically, based on SAM, SAMCT is further equipped with a U-shaped CNN image encoder, a cross-branch interaction module, and a task-indicator prompt encoder.
FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise
Specifically, noise estimation at each client is accomplished through the Gaussian mixture model and then incorporated into model aggregation in a layer-wise manner to up-weight high-quality clients.
SAMUS: Adapting Segment Anything Model for Clinically-Friendly and Generalizable Ultrasound Image Segmentation
Segment anything model (SAM), an eminent universal image segmentation model, has recently gathered considerable attention within the domain of medical image segmentation.
ConvFormer: Plug-and-Play CNN-Style Transformers for Improving Medical Image Segmentation
Following CSA, 2D convolution is utilized for feature refinement through CFFN.
FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity
Federated noisy label learning (FNLL) is emerging as a promising tool for privacy-preserving multi-source decentralized learning.
FCA: Taming Long-tailed Federated Medical Image Classification by Classifier Anchoring
To overcome this, we propose federated classifier anchoring (FCA) by adding a personalized classifier at each client to guide and debias the federated model through consistency learning.
Affinity Feature Strengthening for Accurate, Complete and Robust Vessel Segmentation
Vessel segmentation is crucial in many medical image applications, such as detecting coronary stenoses, retinal vessel diseases and brain aneurysms.
The Lighter The Better: Rethinking Transformers in Medical Image Segmentation Through Adaptive Pruning
To our best knowledge, this is the first work on transformer pruning for medical image analysis tasks.
BATFormer: Towards Boundary-Aware Lightweight Transformer for Efficient Medical Image Segmentation
Specifically, to fully explore the benefits of transformers in long-range dependency establishment, a cross-scale global transformer (CGT) module is introduced to jointly utilize multiple small-scale feature maps for richer global features with lower computational complexity.
FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image Classification
In this paper, we present a privacy-preserving FL method named FedIIC to combat class imbalance from two perspectives: feature learning and classifier learning.
FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation
To relax this assumption, in this work, we propose a label-agnostic unified federated learning framework, named FedMix, for medical image segmentation based on mixed image labels.
Exploring intermediate representation for monocular vehicle pose estimation
The latter question motivates us to incorporate geometry knowledge with a new loss function based on a projective invariant.
Ranked #1 on
Vehicle Pose Estimation
on KITTI Cars Hard
