50 citations · 104 across the 9 of their papers we have counts for
10 papers
Dense Temporal Contrast Synthesis via Conditioned Latent Transport
Smriti Joshi, Apostolia Tsirikoglou, Daniel M. Lang +15
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is essential for breast cancer management, but reliance on gadolinium-based contrast agents (GBCAs) restricts use in…
Simulating Dynamic Tumor Contrast Enhancement in Breast MRI using Conditional Generative Adversarial Networks
Richard Osuala, Smriti Joshi, Apostolia Tsirikoglou +6
This paper presents a method for virtual contrast enhancement in breast MRI, offering a promising non-invasive alternative to traditional contrast agent-based DCE-MRI acquisition.…
A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations
Lidia Garrucho, Kaisar Kushibar, Claire-Anne Reidel +30
Artificial Intelligence (AI) research in breast cancer Magnetic Resonance Imaging (MRI) faces challenges due to limited expert-labeled segmentations. To address this, we present a…
Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Models
Richard Osuala, Daniel M. Lang, Preeti Verma +9
Contrast agents in dynamic contrast enhanced magnetic resonance imaging allow to localize tumors and observe their contrast kinetics, which is essential for cancer characterization…
Pre- to Post-Contrast Breast MRI Synthesis for Enhanced Tumour Segmentation
Richard Osuala, Smriti Joshi, Apostolia Tsirikoglou +4
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, incl…
Primary Tumor and Inter-Organ Augmentations for Supervised Lymph Node Colon Adenocarcinoma Metastasis Detection
Apostolia Tsirikoglou, Karin Stacke, Gabriel Eilertsen +1
The scarcity of labeled data is a major bottleneck for developing accurate and robust deep learning-based models for histopathology applications. The problem is notably prominent f…
How to cheat with metrics in single-image HDR reconstruction
Gabriel Eilertsen, Saghi Hajisharif, Param Hanji +3
Single-image high dynamic range (SI-HDR) reconstruction has recently emerged as a problem well-suited for deep learning methods. Each successive technique demonstrates an improveme…
Ensembles of GANs for synthetic training data generation
Gabriel Eilertsen, Apostolia Tsirikoglou, Claes Lundström +1
Insufficient training data is a major bottleneck for most deep learning practices, not least in medical imaging where data is difficult to collect and publicly available datasets a…
A Study of Deep Learning Colon Cancer Detection in Limited Data Access Scenarios
Apostolia Tsirikoglou, Karin Stacke, Gabriel Eilertsen +2
Digitization of histopathology slides has led to several advances, from easy data sharing and collaborations to the development of digital diagnostic tools. Deep learning (DL) meth…
Procedural Modeling and Physically Based Rendering for Synthetic Data Generation in Automotive Applications
Apostolia Tsirikoglou, Joel Kronander, Magnus Wrenninge +1
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 vi…