8 citations · 11 across the 7 of their papers we have counts for
9 papers
Explicit and data-Efficient Encoding via Gradient Flow
Kyriakos Flouris, Anna Volokitin, Gustav Bredell +1
The autoencoder model typically uses an encoder to map data to a lower dimensional latent space and a decoder to reconstruct it. However, relying on an encoder for inversion can le…
The Importance of Downstream Networks in Digital Pathology Foundation Models
Gustav Bredell, Marcel Fischer, Przemyslaw Szostak +2
Digital pathology has significantly advanced disease detection and pathologist efficiency through the analysis of gigapixel whole-slide images (WSI). In this process, WSIs are firs…
Explicitly Minimizing the Blur Error of Variational Autoencoders
Gustav Bredell, Kyriakos Flouris, Krishna Chaitanya +2
Variational autoencoders (VAEs) are powerful generative modelling methods, however they suffer from blurry generated samples and reconstructions compared to the images they have be…
Unsupervised Superpixel Generation using Edge-Sparse Embedding
Jakob Geusen, Gustav Bredell, Tianfei Zhou +1
Partitioning an image into superpixels based on the similarity of pixels with respect to features such as colour or spatial location can significantly reduce data complexity and im…
Wiener Guided DIP for Unsupervised Blind Image Deconvolution
Gustav Bredell, Ertunc Erdil, Bruno Weber +1
Blind deconvolution is an ill-posed problem arising in various fields ranging from microscopy to astronomy. The ill-posed nature of the problem requires adequate priors to arrive t…
ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image Prior
Metin Ersin Arican, Ozgur Kara, Gustav Bredell +1
Recent works show that convolutional neural network (CNN) architectures have a spectral bias towards lower frequencies, which has been leveraged for various image restoration tasks…
Quality-Aware Memory Network for Interactive Volumetric Image Segmentation
Tianfei Zhou, Liulei Li, Gustav Bredell +2
Despite recent progress of automatic medical image segmentation techniques, fully automatic results usually fail to meet the clinical use and typically require further refinement.…
Gradient flow encoding with distance optimization adaptive step size
Kyriakos Flouris, Anna Volokitin, Gustav Bredell +1
The autoencoder model uses an encoder to map data samples to a lower dimensional latent space and then a decoder to map the latent space representations back to the data space. Imp…
Iterative Interaction Training for Segmentation Editing Networks
Gustav Bredell, Christine Tanner, Ender Konukoglu
Automatic segmentation has great potential to facilitate morphological measurements while simultaneously increasing efficiency. Nevertheless often users want to edit the segmentati…