Explore how AI phenotypic screening transforms image-based drug discovery through advanced phenotypic data analysis and ML-driven cell-based assays.

Abstract: This paper proposes a novel self-supervised clustering framework for automatic defect annotation in semiconductor manufacturing, aiming to reduce the heavy reliance on expert-labeled data.

Abstract: Recent advances in deep learning have dramatically improved the performance of content-based remote sensing image retrieval (CBRSIR) with the same distribution of training set (source domain ...

Unsupervised Camoflaged Object Detection (UCOD) has gained attention since it doesn’t need to rely on extensive pixel-level labels. Existing UCOD methods typically generate pseudo-labels using fixed ...

Our methodology demonstrates a proof of concept of the applicability of transfer learning for heliophysics, a machine learning technique where knowledge learned from one task is reused to perform a ...

Automatic detection of macromolecular complexes is an open and challenging problem in cellular cryoelectron tomography. Existing computational methods rely on known structural templates or manually ...

Combining clustering and representation learning is one of the most promising approaches for unsupervised learning of deep neural networks. However, doing so naively leads to ill posed learning ...

Learning from limited exemplars (few-shot learning) is a fundamental, unsolved problem that has been laboriously explored in the machine learning community. However, current few-shot learners are ...