Search Results for author:
Found 16 papers, 4 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.
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)
MIGS: Meta Image Generation from Scene Graphs
We propose MIGS (Meta Image Generation from Scene Graphs), a meta-learning based approach for few-shot image generation from graphs that enables adapting the model to different scenes and increases the image quality by training on diverse sets of tasks.
Fine-Grained Neural Network Explanation by Identifying Input Features with Predictive Information
We propose a method to identify features with predictive information in the input domain.
MetaMedSeg: Volumetric Meta-learning for Few-Shot Organ Segmentation
In this work, we present MetaMedSeg, a gradient-based meta-learning algorithm that redefines the meta-learning task for the volumetric medical data with the goal to capture the variety between the slices.
Unconditional Scene Graph Generation
Scene graphs, composed of nodes as objects and directed-edges as relationships among objects, offer an alternative representation of a scene that is more semantically grounded than images.
Meta-k: Towards Unsupervised Prediction of Number of Clusters
Data clustering is a well-known unsupervised learning approach.
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.
Semantic Image Manipulation Using Scene Graphs
In our work, we address the novel problem of image manipulation from scene graphs, in which a user can edit images by merely applying changes in the nodes or edges of a semantic graph that is generated from the image.
Adversarial Network Compression
Neural network compression has recently received much attention due to the computational requirements of modern deep models.
