105 citations · 131 across the 6 of their papers we have counts for
6 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…
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…
Is it Time to Replace CNNs with Transformers for Medical Images?
Christos Matsoukas, Johan Fredin Haslum, Magnus Söderberg +1
Convolutional Neural Networks (CNNs) have reigned for a decade as the de facto approach to automated medical image diagnosis. Recently, vision transformers (ViTs) have appeared as…
Adding Seemingly Uninformative Labels Helps in Low Data Regimes
Christos Matsoukas, Albert Bou I Hernandez, Yue Liu +8
Evidence suggests that networks trained on large datasets generalize well not solely because of the numerous training examples, but also class diversity which encourages learning o…