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Found 7 papers, 3 papers with code
EchoFM: Foundation Model for Generalizable Echocardiogram Analysis
In this paper, we introduce EchoFM, a foundation model specifically designed to represent and analyze echocardiography videos.
Retrieval Instead of Fine-tuning: A Retrieval-based Parameter Ensemble for Zero-shot Learning
Foundation models have become a cornerstone in deep learning, with techniques like Low-Rank Adaptation (LoRA) offering efficient fine-tuning of large models.
ECHOPulse: ECG controlled echocardio-grams video generation
Echocardiography (ECHO) is essential for cardiac assessments, but its video quality and interpretation heavily relies on manual expertise, leading to inconsistent results from clinical and portable devices.
Implicit Image-to-Image Schrodinger Bridge for Image Restoration
The Image-to-Image Schr\"odinger Bridge (I$^2$SB) presents a promising alternative by starting the generative process from corrupted images and leveraging training techniques from score-based diffusion models.
MediViSTA: Medical Video Segmentation via Temporal Fusion SAM Adaptation for Echocardiography
In this paper, we introduce MediViSTA, a parameter-efficient fine-tuning method designed to adapt the vision foundation model for medical video, with a specific focus on echocardiographic segmentation.
Machine learning assisted exploration for affine Deligne-Lusztig varieties
We demonstrate that this framework has a potential to accelerate pure mathematical research, leading to the discovery of new conjectures and promising research directions that could otherwise take significant time to uncover.
NPTC-net: Narrow-Band Parallel Transport Convolutional Neural Network on Point Clouds
Designing appropriate convolution neural networks on manifold-structured point clouds can inherit and empower recent advances of CNNs to analyzing and processing point cloud data.
