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Found 6 papers, 0 papers with code
OPTiML: Dense Semantic Invariance Using Optimal Transport for Self-Supervised Medical Image Representation
In response to these constraints, we introduce a novel SSL framework OPTiML, employing optimal transport (OT), to capture the dense semantic invariance and fine-grained details, thereby enhancing the overall effectiveness of SSL in medical image representation learning.
MLVICX: Multi-Level Variance-Covariance Exploration for Chest X-ray Self-Supervised Representation Learning
The performance enhancements we observe across various downstream tasks highlight the significance of the proposed approach in enhancing the utility of chest X-ray embeddings for precision medical diagnosis and comprehensive image analysis.
Harmonized Spatial and Spectral Learning for Robust and Generalized Medical Image Segmentation
This paper presents a novel approach that synergies spatial and spectral representations to enhance domain-generalized medical image segmentation.
Rethinking Intermediate Layers design in Knowledge Distillation for Kidney and Liver Tumor Segmentation
HLFD strategically distills knowledge from a combination of middle layers to earlier layers and transfers final layer knowledge to intermediate layers at both the feature and pixel levels.
SynergyNet: Bridging the Gap between Discrete and Continuous Representations for Precise Medical Image Segmentation
When evaluating skin lesion and brain tumor segmentation datasets, we observe a remarkable improvement of 1. 71% in Intersection-over Union scores for skin lesion segmentation and of 8. 58% for brain tumor segmentation.
Large Scale Time-Series Representation Learning via Simultaneous Low and High Frequency Feature Bootstrapping
To tackle these problems, we propose a non-contrastive self-supervised learning approach efficiently captures low and high-frequency time-varying features in a cost-effective manner.
