617 citations · 888 across the 12 of their papers we have counts for
13 papers
Standalone Neural ODEs with Sensitivity Analysis
Rym Jaroudi, Lukáš Malý, Gabriel Eilertsen +3
This paper presents the Standalone Neural ODE (sNODE), a continuous-depth neural ODE model capable of describing a full deep neural network. This uses a novel nonlinear conjugate g…
Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications
Karin Stacke, Jonas Unger, Claes Lundström +1
Unsupervised learning has made substantial progress over the last few years, especially by means of contrastive self-supervised learning. The dominating dataset for benchmarking se…
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…
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…
Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing
Magnus Wrenninge, Jonas Unger
We introduce Synscapes -- a synthetic dataset for street scene parsing created using photorealistic rendering techniques, and show state-of-the-art results for training and validat…
HDR image reconstruction from a single exposure using deep CNNs
Gabriel Eilertsen, Joel Kronander, Gyorgy Denes +2
Camera sensors can only capture a limited range of luminance simultaneously, and in order to create high dynamic range (HDR) images a set of different exposures are typically combi…
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…
A Unified Framework for Multi-Sensor HDR Video Reconstruction
Joel Kronander, Stefan Gustavson, Gerhard Bonnet +2
One of the most successful approaches to modern high quality HDR-video capture is to use camera setups with multiple sensors imaging the scene through a common optical system. Howe…