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As a standard method for neuroautoantibody detection, pathologists use tissue matrix assays (TBA) for initial disease screening. In this study, microscopic fluorescence imaging was combined with deep learning to improve AE diagnostic accuracy. Due to the inter\u2010class imbalance of medical data, we propose an innovative generative adversarial network supplemented with attention mechanisms to highlight key regions in images to synthesize high\u2010quality fluorescence images. However, securing annotated medical data is both time\u2010consuming and costly. To circumvent this problem, we employ a self\u2010supervised learning approach that utilizes unlabeled fluorescence data to support downstream classification tasks. To better understand the fluorescence properties in the data, we introduce a multichannel input convolutional neural network that adds additional channels of fluorescence intensity. 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