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Found 7 papers, 3 papers with code
Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Models
Contrast agents in dynamic contrast enhanced magnetic resonance imaging allow to localize tumors and observe their contrast kinetics, which is essential for cancer characterization and respective treatment decision-making.
Pre- to Post-Contrast Breast MRI Synthesis for Enhanced Tumour Segmentation
Despite its benefits for tumour detection and treatment, the administration of contrast agents in dynamic contrast-enhanced MRI (DCE-MRI) is associated with a range of issues, including their invasiveness, bioaccumulation, and a risk of nephrogenic systemic fibrosis.
Primary Tumor and Inter-Organ Augmentations for Supervised Lymph Node Colon Adenocarcinoma Metastasis Detection
The scarcity of labeled data is a major bottleneck for developing accurate and robust deep learning-based models for histopathology applications.
How to cheat with metrics in single-image HDR reconstruction
Here, we reproduce a typical evaluation using existing as well as simulated SI-HDR methods to demonstrate how different aspects of the problem affect objective quality metrics.
Ensembles of GANs for synthetic training data generation
This work investigates the use of synthetic images, created by generative adversarial networks (GANs), as the only source of training data.
A Study of Deep Learning Colon Cancer Detection in Limited Data Access Scenarios
One such scenario relates to detecting tumor metastasis in lymph node tissue, where the low ratio of tumor to non-tumor cells makes the diagnostic task hard and time-consuming.
Procedural Modeling and Physically Based Rendering for Synthetic Data Generation in Automotive Applications
We present an overview and evaluation of a new, systematic approach for generation of highly realistic, annotated synthetic data for training of deep neural networks in computer vision tasks.
