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Found 10 papers, 1 papers with code
PRISM: Progressive Restoration for Scene Graph-based Image Manipulation
We take advantage of the outer part of the masked area as they have a direct correlation with the context of the scene.
AutoPaint: A Self-Inpainting Method for Unsupervised Anomaly Detection
This work intends to, first, propose a robust inpainting model to learn the details of healthy anatomies and reconstruct high-resolution images by preserving anatomical constraints.
SceneGenie: Scene Graph Guided Diffusion Models for Image Synthesis
To address this limitation, we propose a novel guidance approach for the sampling process in the diffusion model that leverages bounding box and segmentation map information at inference time without additional training data.
SCOPE: Structural Continuity Preservation for Medical Image Segmentation
Although the preservation of shape continuity and physiological anatomy is a natural assumption in the segmentation of medical images, it is often neglected by deep learning methods that mostly aim for the statistical modeling of input data as pixels rather than interconnected structures.
DIAMANT: Dual Image-Attention Map Encoders For Medical Image Segmentation
Although purely transformer-based architectures showed promising performance in many computer vision tasks, many hybrid models consisting of CNN and transformer blocks are introduced to fit more specialized tasks.
DisPositioNet: Disentangled Pose and Identity in Semantic Image Manipulation
Graph representation of objects and their relations in a scene, known as a scene graph, provides a precise and discernible interface to manipulate a scene by modifying the nodes or the edges in the graph.
Shape-Aware Masking for Inpainting in Medical Imaging
Inpainting has recently been proposed as a successful deep learning technique for unsupervised medical image model discovery.
Adaptive Personlization in Federated Learning for Highly Non-i.i.d. Data
Although most medical centers conduct similar medical imaging tasks, their differences, such as specializations, number of patients, and devices, lead to distinctive data distributions.
Y-Net: A Spatiospectral Dual-Encoder Networkfor Medical Image Segmentation
Automated segmentation of retinal optical coherence tomography (OCT) images has become an important recent direction in machine learning for medical applications.
Ranked #1 on
Retinal OCT Layer Segmentation
on Duke SD-OCT
(using extra training data)
Inverse Distance Aggregation for Federated Learning with Non-IID Data
Federated learning (FL) has been a promising approach in the field of medical imaging in recent years.
