RABC-Net achieves 86.58% DICE and 79.47% JAC on skin lesion segmentation across ISIC-2017, ISIC-2018, and PH2 using only pseudo-labels and no manual masks for training or adaptation.
author Xiang, S
2 Pith papers cite this work. Polarity classification is still indexing.
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Fine-tuning pre-trained embeddings is necessary for best performance in predicting AAV vector viability, with sequence-level representations excelling post-fine-tuning in datasets with sparse localized mutations.
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RABC-Net: Reliability-Aware Annotation-Free Skin Lesion Segmentation for Low-Resource Dermoscopy
RABC-Net achieves 86.58% DICE and 79.47% JAC on skin lesion segmentation across ISIC-2017, ISIC-2018, and PH2 using only pseudo-labels and no manual masks for training or adaptation.
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Exploring the limits of pre-trained embeddings in machine-guided protein design: a case study on predicting AAV vector viability
Fine-tuning pre-trained embeddings is necessary for best performance in predicting AAV vector viability, with sequence-level representations excelling post-fine-tuning in datasets with sparse localized mutations.