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.

利用基于分割的 AdaSyn 框架和弱点注释，本研究提出了一种用于领域自适应突触检测的两阶段分割方法，通过高质量伪标签改善模型的普适性，成功应用于不同脑区的数据集，并在 ISBI 2023 的 WASPSYN 挑战中获得第一名。

弱点注释下的领域自适应突触检测

Domain Adaptive Synapse Detection with Weak Point Annotations

The present study aimed to address the issue of imbalanced data in
classification tasks and evaluated the suitability of SMOTE, ADASYN, and GAN
techniques in generating synthetic data to address the class imbalance and
improve the performance of classification models in low-resource settings. The
study employed the Generalised Linear Model (GLM) algorithm for class balancing
experiments and the Random Forest (RF) algorithm for low-resource setting
experiments to assess model performance under varying training data. The recall
metric was the primary evaluation metric for all classification models. The
results of the class balancing experiments showed that the GLM model trained on
GAN-balanced data achieved the highest recall value. Similarly, in low-resource
experiments, models trained on data enhanced with GAN-synthesized data
exhibited better recall values than original data. These findings demonstrate
the potential of GAN-generated synthetic data for addressing the challenge of
imbalanced data in classification tasks and improving model performance in
low-resource settings.

本研究旨在解决分类任务中不平衡数据的问题，并评估 SMOTE、ADASYN 和 GAN 技术在生成合成数据以解决类别不平衡和提高分类模型在低资源环境下的性能方面的适用性。