167 citations · 304 across the 10 of their papers we have counts for
11 papers
Pretrained ViTs Yield Versatile Representations For Medical Images
Christos Matsoukas, Johan Fredin Haslum, Moein Sorkhei +2
Convolutional Neural Networks (CNNs) have reigned for a decade as the de facto approach to automated medical image diagnosis, pushing the state-of-the-art in classification, detect…
Metadata-guided Consistency Learning for High Content Images
Johan Fredin Haslum, Christos Matsoukas, Karl-Johan Leuchowius +2
High content imaging assays can capture rich phenotypic response data for large sets of compound treatments, aiding in the characterization and discovery of novel drugs. However, e…
Wide Range MRI Artifact Removal with Transformers
Lennart Alexander Van der Goten, Kevin Smith
Artifacts on magnetic resonance scans are a serious challenge for both radiologists and computer-aided diagnosis systems. Most commonly, artifacts are caused by motion of the patie…
PatchDropout: Economizing Vision Transformers Using Patch Dropout
Yue Liu, Christos Matsoukas, Fredrik Strand +2
Vision transformers have demonstrated the potential to outperform CNNs in a variety of vision tasks. But the computational and memory requirements of these models prohibit their us…
What Makes Transfer Learning Work For Medical Images: Feature Reuse & Other Factors
Christos Matsoukas, Johan Fredin Haslum, Moein Sorkhei +2
Transfer learning is a standard technique to transfer knowledge from one domain to another. For applications in medical imaging, transfer from ImageNet has become the de-facto appr…
Conditional De-Identification of 3D Magnetic Resonance Images
Lennart Alexander Van der Goten, Tobias Hepp, Zeynep Akata +1
Privacy protection of medical image data is challenging. Even if metadata is removed, brain scans are vulnerable to attacks that match renderings of the face to facial image databa…