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Found 6 papers, 3 papers with code
FedSODA: Federated Cross-assessment and Dynamic Aggregation for Histopathology Segmentation
Federated learning (FL) for histopathology image segmentation involving multiple medical sites plays a crucial role in advancing the field of accurate disease diagnosis and treatment.
Dynamic Snake Convolution based on Topological Geometric Constraints for Tubular Structure Segmentation
In this work, we note the specificity of tubular structures and use this knowledge to guide our DSCNet to simultaneously enhance perception in three stages: feature extraction, feature fusion, and loss constraint.
Contrastive Re-localization and History Distillation in Federated CMR Segmentation
In this work, a cross-center cross-sequence medical image segmentation FL framework (FedCRLD) is proposed for the first time to facilitate multi-center multi-sequence CMR segmentation.
MNet: Rethinking 2D/3D Networks for Anisotropic Medical Image Segmentation
The nature of thick-slice scanning causes severe inter-slice discontinuities of 3D medical images, and the vanilla 2D/3D convolutional neural networks (CNNs) fail to represent sparse inter-slice information and dense intra-slice information in a balanced way, leading to severe underfitting to inter-slice features (for vanilla 2D CNNs) and overfitting to noise from long-range slices (for vanilla 3D CNNs).
EnMcGAN: Adversarial Ensemble Learning for 3D Complete Renal Structures Segmentation
3)We propose the adversarial weighted ensemble module which uses the trained discriminators to evaluate the quality of segmented structures, and normalizes these evaluation scores for the ensemble weights directed at the input image, thus enhancing the ensemble results.
Deep Reinforcement Learning for Imbalanced Classification
The agent finally finds an optimal classification policy in imbalanced data under the guidance of specific reward function and beneficial learning environment.
