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- W4386395628 abstract "The development of learning-based methods has greatly improved the detection of synapses from electron microscopy (EM) images. However, training a model for each dataset is time-consuming and requires extensive annotations. Additionally, it is difficult to apply a learned model to data from different brain regions due to variations in data distributions. In this paper, we present AdaSyn, a two-stage segmentation-based framework for domain adaptive synapse detection with weak point annotations. In the first stage, we address the detection problem by utilizing a segmentation-based pipeline to obtain synaptic instance masks. In the second stage, we improve model generalizability on target data by regenerating square masks to get high-quality pseudo labels. Benefiting from our high-accuracy detection results, we introduce the distance nearest principle to match paired pre-synapses and post-synapses. In the WASPSYN challenge at ISBI 2023, our method ranks the 1st place." @default.
- W4386395628 created "2023-09-03" @default.
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- W4386395628 date "2023-08-31" @default.
- W4386395628 modified "2023-09-27" @default.
- W4386395628 title "Domain Adaptive Synapse Detection with Weak Point Annotations" @default.
- W4386395628 doi "https://doi.org/10.48550/arxiv.2308.16461" @default.
- W4386395628 hasPublicationYear "2023" @default.
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