| Is it time to replace cnns with transformers for medical images? C Matsoukas, JF Haslum, M Söderberg, K Smith arXiv preprint arXiv:2108.09038, 2021 | 139 | 2021 |
| What makes transfer learning work for medical images: feature reuse & other factors C Matsoukas, JF Haslum, M Sorkhei, M Söderberg, K Smith Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 71 | 2022 |
| Adding seemingly uninformative labels helps in low data regimes C Matsoukas, AB Hernandez, Y Liu, K Dembrower, G Miranda, E Konuk, ... International Conference on Machine Learning, 6775-6784, 2020 | 14 | 2020 |
| Pretrained ViTs yield versatile representations for medical images C Matsoukas, JF Haslum, M Söderberg, K Smith arXiv preprint arXiv:2303.07034, 2023 | 8* | 2023 |
| Metadata-guided consistency learning for high content images JF Haslum, C Matsoukas, KJ Leuchowius, E Müllers, K Smith Medical Imaging with Deep Learning, 918-936, 2024 | 4 | 2024 |
| Are Natural Domain Foundation Models Useful for Medical Image Classification? JP Huix, AR Ganeshan, JF Haslum, M Söderberg, C Matsoukas, K Smith Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2024 | 2 | 2024 |
| Cell Painting-based bioactivity prediction boosts high-throughput screening hit-rates and compound diversity J Fredin Haslum, CH Lardeau, J Karlsson, R Turkki, KJ Leuchowius, ... Nature Communications 15 (1), 3470, 2024 | 1 | 2024 |
| Bridging Generalization Gaps in High Content Imaging Through Online Self-Supervised Domain Adaptation JF Haslum, C Matsoukas, KJ Leuchowius, K Smith Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2024 | | 2024 |
