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ProtoGMM: Multi-prototype Gaussian-Mixture-based Domain Adaptation Model for Semantic Segmentation

Autor: Moradinasab, Nazanin, Shankman, Laura S., Deaton, Rebecca A., Owens, Gary K., Brown, Donald E.

Domain adaptive semantic segmentation aims to generate accurate and dense predictions for an unlabeled target domain by leveraging a supervised model trained on a labeled source domain. The prevalent self-training approach involves retraining the den

Externí odkaz: http://arxiv.org/abs/2406.19225

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Label-efficient Contrastive Learning-based model for nuclei detection and classification in 3D Cardiovascular Immunofluorescent Images

Autor: Moradinasab, Nazanin, Deaton, Rebecca A., Shankman, Laura S., Owens, Gary K., Brown, Donald E.

Recently, deep learning-based methods achieved promising performance in nuclei detection and classification applications. However, training deep learning-based methods requires a large amount of pixel-wise annotated data, which is time-consuming and

Externí odkaz: http://arxiv.org/abs/2309.03744

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Weakly Supervised Deep Instance Nuclei Detection using Points Annotation in 3D Cardiovascular Immunofluorescent Images

Autor: Moradinasab, Nazanin, Sharma, Yash, Shankman, Laura S., Owens, Gary K., Brown, Donald E.

Two major causes of death in the United States and worldwide are stroke and myocardial infarction. The underlying cause of both is thrombi released from ruptured or eroded unstable atherosclerotic plaques that occlude vessels in the heart (myocardial

Externí odkaz: http://arxiv.org/abs/2208.00098

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