617 citations · 718 across the 8 of their papers we have counts for
7 papers · 1 filter
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…
Survey of XAI in digital pathology
Milda Pocevičiūtė, Gabriel Eilertsen, Claes Lundström
Artificial intelligence (AI) has shown great promise for diagnostic imaging assessments. However, the application of AI to support medical diagnostics in clinical routine comes wit…
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…
Single-frame Regularization for Temporally Stable CNNs
Gabriel Eilertsen, Rafał K. Mantiuk, Jonas Unger
Convolutional neural networks (CNNs) can model complicated non-linear relations between images. However, they are notoriously sensitive to small changes in the input. Most CNNs tra…