105 citations · 135 across the 9 of their papers we have counts for
10 papers
Same Encoder, Different Winner: A Paired-View Framework for Cell Painting Encoder Evaluation
Tim Treis, Nikita Moshkov, Johan Fredin Haslum +2
Vision encoders for Cell Painting are typically ranked by a single evaluation, commonly replicate mean average precision (mAP). We introduce CP-BG-Bench, a paired-view evaluation f…
JUMP-lite: Compact, reproducible benchmarking of cell representations
Alán F. Muñoz, Johan Fredin Haslum, Runxi Shen +2
Image-based profiling captures rich phenotypic signatures for drug discovery and functional genomics. Large public datasets like JUMP Cell Painting now provide millions of images f…
k-NN as a Simple and Effective Estimator of Transferability
Moein Sorkhei, Christos Matsoukas, Johan Fredin Haslum +2
How well can one expect transfer learning to work in a new setting where the domain is shifted, the task is different, and the architecture changes? Many transfer learning metrics…
Bridging Generalization Gaps in High Content Imaging Through Online Self-Supervised Domain Adaptation
Johan Fredin Haslum, Christos Matsoukas, Karl-Johan Leuchowius +1
High Content Imaging (HCI) plays a vital role in modern drug discovery and development pipelines, facilitating various stages from hit identification to candidate drug characteriza…
Are Natural Domain Foundation Models Useful for Medical Image Classification?
Joana Palés Huix, Adithya Raju Ganeshan, Johan Fredin Haslum +3
The deep learning field is converging towards the use of general foundation models that can be easily adapted for diverse tasks. While this paradigm shift has become common practic…
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…
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…