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The reliability and accuracy of artificial intelligence (AI) algorithms can degrade when such algorithms are applied to new, varied datasets — a common challenge known as “distribution shift”. For example, an AI model trained to detect tumors in MRI scans from a single hospital may not perform well on scans from another hospital. To address this issue, we aim to adapt the internal representations of “foundation models”, which are pre-trained on diverse data without specific labels. These adapted representations may allow us to partially mitigate distribution shift and boost the model’s performance in a broader environment.
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