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Found 7 papers, 0 papers with code
Pre-training Everywhere: Parameter-Efficient Fine-Tuning for Medical Image Analysis via Target Parameter Pre-training
During this stage, the pre-trained backbone parameters are frozen, and only the target parameters are trainable.
From Few to More: Scribble-based Medical Image Segmentation via Masked Context Modeling and Continuous Pseudo Labels
MaCo employs masked context modeling (MCM) and continuous pseudo labels (CPL).
Grouping Boundary Proposals for Fast Interactive Image Segmentation
These boundary proposals are then incorporated into the proposed image segmentation model, such that the target segmentation contours are made up of a set of selected boundary proposals and the corresponding geodesic paths linking them.
Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computation
Guided by FL convergence analysis, we formulate a joint transmission probability and local computing control optimization, aiming to minimize the overall energy consumption (i. e., iterative local computing + multi-round communications) of mobile devices in FL.
Geodesic Models with Convexity Shape Prior
The minimal geodesic models based on the Eikonal equations are capable of finding suitable solutions in various image segmentation scenarios.
To Talk or to Work: Delay Efficient Federated Learning over Mobile Edge Devices
Federated learning (FL), an emerging distributed machine learning paradigm, in conflux with edge computing is a promising area with novel applications over mobile edge devices.
A Generalized Asymmetric Dual-front Model for Active Contours and Image Segmentation
The Voronoi diagram-based dual-front active contour models are known as a powerful and efficient way for addressing the image segmentation and domain partitioning problems.
