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Optimizing Transformations for Contrastive Learning in a Differentiable Framework

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Medical Image Learning with Limited and Noisy Data (MILLanD 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13559))

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Abstract

Current contrastive learning methods use random transformations sampled from a large list of transformations, with fixed hyper-parameters, to learn invariance from an unannotated database. Following previous works that introduce a small amount of supervision, we propose a framework to find optimal transformations for contrastive learning using a differentiable transformation network. Our method increases performances at low annotated data regime both in supervision accuracy and in convergence speed. In contrast to previous work, no generative model is needed for transformation optimization. Transformed images keep relevant information to solve the supervised task, here classification. Experiments were performed on 34000 2D slices of brain Magnetic Resonance Images and 11200 chest X-ray images. On both datasets, with 10% of labeled data, our model achieves better performances than a fully supervised model with 100% labels.

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Correspondence to Camille Ruppli .

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Ruppli, C., Gori, P., Ardon, R., Bloch, I. (2022). Optimizing Transformations for Contrastive Learning in a Differentiable Framework. In: Zamzmi, G., Antani, S., Bagci, U., Linguraru, M.G., Rajaraman, S., Xue, Z. (eds) Medical Image Learning with Limited and Noisy Data. MILLanD 2022. Lecture Notes in Computer Science, vol 13559. Springer, Cham. https://doi.org/10.1007/978-3-031-16760-7_10

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  • DOI: https://doi.org/10.1007/978-3-031-16760-7_10

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  • Online ISBN: 978-3-031-16760-7

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