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Found 8 papers, 2 papers with code
PE-MED: Prompt Enhancement for Interactive Medical Image Segmentation
It can prevent the highly unfavorable scenarios, such as encountering a blank mask as the initial input after the first interaction.
OnUVS: Online Feature Decoupling Framework for High-Fidelity Ultrasound Video Synthesis
Second, to better preserve the integrity and textural information of US images, we implemented a dual-decoder that decouples the content and textural features in the generator.
Inflated 3D Convolution-Transformer for Weakly-supervised Carotid Stenosis Grading with Ultrasound Videos
First, to avoid the requirement of laborious and unreliable annotation, we propose a novel and effective video classification network for weakly-supervised CSG.
Fourier Test-time Adaptation with Multi-level Consistency for Robust Classification
Second, we introduce a regularization technique that utilizes style interpolation consistency in the frequency space to encourage self-consistency in the logit space of the model output.
Segment Anything Model for Medical Images?
To fully validate SAM's performance on medical data, we collected and sorted 53 open-source datasets and built a large medical segmentation dataset with 18 modalities, 84 objects, 125 object-modality paired targets, 1050K 2D images, and 6033K masks.
Online Reflective Learning for Robust Medical Image Segmentation
Deep segmentation models often face the failure risks when the testing image presents unseen distributions.
Weakly-supervised High-fidelity Ultrasound Video Synthesis with Feature Decoupling
First, leveraging the advantages of self- and fully-supervised learning, our proposed system is trained in weakly-supervised manner for keypoint detection.
Fine-grained Correlation Loss for Regression
In this work, we propose to revisit the classic regression tasks with novel investigations on directly optimizing the fine-grained correlation losses.
