The field of microscopy image analysis is rapidly advancing, with a growing focus on developing innovative methods for image classification, object detection, and segmentation. Recent research has explored the use of time series data to improve the accuracy of microscopy image classification, while others have investigated the application of disentangled representation learning to enhance model interpretability. Additionally, there is a increasing interest in developing robust defense mechanisms against adversarial attacks, which pose a significant threat to the security of deep neural networks. Researchers are proposing novel methods for detecting and mitigating these attacks, including the use of nonuniform impact on network layers and style-aligned image composition. Noteworthy papers in this area include: Towards Classifying Histopathological Microscope Images as Time Series Data, which proposes a novel approach to classifying microscopy images as time series data. SafeTriage: Facial Video De-identification for Privacy-Preserving Stroke Triage, which introduces a method for de-identifying patient facial videos while preserving essential motion cues for stroke diagnosis. Universal and Efficient Detection of Adversarial Data through Nonuniform Impact on Network Layers, which proposes a novel method for detecting adversarial examples by analyzing the varying degrees of impact of attacks on different DNN layers. Style-Aligned Image Composition for Robust Detection of Abnormal Cells in Cytopathology, which proposes a style-aligned image composition method to enhance the effectiveness and robustness of detection models.