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Found 7 papers, 4 papers with code
Endora: Video Generation Models as Endoscopy Simulators
In a nutshell, Endora marks a notable breakthrough in the deployment of generative AI for clinical endoscopy research, setting a substantial stage for further advances in medical content generation.
Adjustment and Alignment for Unbiased Open Set Domain Adaptation
In a nutshell, ANNA consists of Front-Door Adjustment (FDA) to correct the biased learning in the source domain and Decoupled Causal Alignment (DCA) to transfer the model unbiasedly.
MRM: Masked Relation Modeling for Medical Image Pre-Training with Genetics
Moreover, to enhance semantic relation modeling, we propose relation matching to align the sample-wise relation between the intact and masked features.
Novel Scenes & Classes: Towards Adaptive Open-set Object Detection
Then, a high-order metric is proposed to match the most significant motif as high-order patterns, serving for motif-guided novel-class learning.
Towards Robust Adaptive Object Detection under Noisy Annotations
Domain Adaptive Object Detection (DAOD) models a joint distribution of images and labels from an annotated source domain and learns a domain-invariant transformation to estimate the target labels with the given target domain images.
SIGMA: Semantic-complete Graph Matching for Domain Adaptive Object Detection
To overcome these challenges, we propose a novel SemantIc-complete Graph MAtching (SIGMA) framework for DAOD, which completes mismatched semantics and reformulates the adaptation with graph matching.
Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge
Polyps are well-known cancer precursors identified by colonoscopy.
