11 citations · 30 across the 12 of their papers we have counts for
5 papers · 1 filter
Deep Learning for BioImaging: What Are We Really Learning?
Ivan Svatko, Maxime Sanchez, Ihab Bendidi +2
Representation learning has driven major advances in natural image analysis by enabling models to acquire high-level semantic features. In microscopy imaging, however, it remains u…
Weakly supervised cross-modal learning in high-content screening
Watkinson Gabriel, Cohen Ethan, Bourriez Nicolas +3
With the surge in available data from various modalities, there is a growing need to bridge the gap between different data types. In this work, we introduce a novel approach to lea…
ChAda-ViT : Channel Adaptive Attention for Joint Representation Learning of Heterogeneous Microscopy Images
Nicolas Bourriez, Ihab Bendidi, Ethan Cohen +4
Unlike color photography images, which are consistently encoded into RGB channels, biological images encompass various modalities, where the type of microscopy and the meaning of e…
No Free Lunch in Self Supervised Representation Learning
Ihab Bendidi, Adrien Bardes, Ethan Cohen +3
Self-supervised representation learning in computer vision relies heavily on hand-crafted image transformations to learn meaningful and invariant features. However few extensive ex…
Comparison of semi-supervised learning methods for High Content Screening quality control
Umar Masud, Ethan Cohen, Ihab Bendidi +2
Progress in automated microscopy and quantitative image analysis has promoted high-content screening (HCS) as an efficient drug discovery and research tool. While HCS offers to qua…