This paper proposes a framework for oversampling the minority mitotic patterns of the HEp-2 cell images. The classification of mitotic vs. non-mitotic (interphase) cell patterns is important for validating the Indirect Immunofluorescence on Human Epithelial Type-2 cell-substrate (IIF HEp-2) protocol, which is the "gold standard"test for diagnosing autoimmune diseases. Typically, the mitotic cells appear in the HEp-2 specimen image in a significantly less number than the interphases. This causes difficulty in adopting deep learning approaches to classify mitotic vs. interphase patterns with such high imbalanced data. This work suggests using One-Dimensional Deep Convolutional Generative Adversarial Networks (1D-DCGAN) for oversampling the minority mitotic patterns in the feature space of the Deep Cross Residual Network (DCRNet) to cope with the data skewness problem. The results demonstrated that the proposed approach improves the classification performance over the UQ-SNP I3A Task-3 mitotic cell dataset with the advantage of using an end-to-end CNN classifier.