The present disclosure relates to computer-implemented systems and methods for training and using generative adversarial networks. In one implementation, a system for training a generative adversarial network may include at least one processor that may provide a first plurality of images including representations of a feature-of-interest and indicators of locations of the feature-of-interest and use the first plurality and indicators to train an object detection network. Further, the processor(s) may provide a second plurality of images including representations of the feature-of-interest, and apply the trained object detection network to the second plurality to produce a plurality of detections of the feature-of-interest. Additionally, the processor(s) may provide manually set verifications of true positives and false positives with respect to the plurality of detections, use the verifications to train a generative adversarial network, and retrain the generative adversarial network using at least one further set of images, further detections, and further manually set verifications.本發明係關於用於訓練及使用生成對抗網路之電腦實施系統及方法。在一實施方案中，一種用於訓練一生成對抗網路之系統可包含至少一處理器，其可提供包含一所關注特徵之表示之第一複數個影像及該所關注特徵之位置之指示符且使用該第一複數個及該等指示符來訓練一物體偵測網路。此外，該(等)處理器可提供包含該所關注特徵之表示之第二複數個影像且將該經訓練之物體偵測網路應用於該第二複數個以產生該所關注特徵之複數個偵測。另外，該(等)處理器可提供關於該複數個偵測之真陽性及假陽性之人工設定驗證，使用該等驗證來訓練一生成對抗網路，且使用至少一進一步影像集、進一步偵測及進一步人工設定驗證來再訓練該生成對抗網路。
