617 citations · 714 across the 7 of their papers we have counts for
10 papers
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…
Classifying the classifier: dissecting the weight space of neural networks
Gabriel Eilertsen, Daniel Jönsson, Timo Ropinski +2
This paper presents an empirical study on the weights of neural networks, where we interpret each model as a point in a high-dimensional space -- the neural weight space. To explor…
A Closer Look at Domain Shift for Deep Learning in Histopathology
Karin Stacke, Gabriel Eilertsen, Jonas Unger +1
Domain shift is a significant problem in histopathology. There can be large differences in data characteristics of whole-slide images between medical centers and scanners, making g…