The paper demonstrates the use of deep convolutional neural networks (DCNNs) combined with nonlinear dimension reduction to reconstruct biological processes from raw image data. Specifically, the authors apply this approach to reconstruct the cell cycle of Jurkat cells and disease progression in diabetic retinopathy. Key findings include: 1. **Cell Cycle Reconstruction**: The network successfully organizes cells into seven distinct stages of the cell cycle, including interphase (G1, S, G2) and mitotic phases (prophase, anaphase, metaphase, telophase), even though the network was not provided with any structural information about the cell cycle. 2. **Abnormal Cell Detection**: The network automatically detects a subpopulation of dead cells in an unsupervised manner, which is not possible with traditional machine learning methods. 3. **Classification Accuracy**: The deep learning model achieves a sixfold improvement in error rate compared to a boosting approach on the same data, achieving 98.73% accuracy for classifying five cell cycle phases. 4. **Disease Progression Reconstruction**: The network reconstructs the progression of diabetic retinopathy, ordered from healthy to severe, without explicit ordering information. 5. **Speed and Practical Applications**: The deep learning model is significantly faster than traditional methods, allowing for real-time analysis in imaging flow cytometry, which can process up to 1000 cells per second. The authors conclude that deep learning is a powerful tool for understanding continuous biological processes and can be applied to a wide range of image data, provided there are enough samples available. The method's flexibility and speed make it particularly useful for high-throughput applications in biology and medicine.The paper demonstrates the use of deep convolutional neural networks (DCNNs) combined with nonlinear dimension reduction to reconstruct biological processes from raw image data. Specifically, the authors apply this approach to reconstruct the cell cycle of Jurkat cells and disease progression in diabetic retinopathy. Key findings include: 1. **Cell Cycle Reconstruction**: The network successfully organizes cells into seven distinct stages of the cell cycle, including interphase (G1, S, G2) and mitotic phases (prophase, anaphase, metaphase, telophase), even though the network was not provided with any structural information about the cell cycle. 2. **Abnormal Cell Detection**: The network automatically detects a subpopulation of dead cells in an unsupervised manner, which is not possible with traditional machine learning methods. 3. **Classification Accuracy**: The deep learning model achieves a sixfold improvement in error rate compared to a boosting approach on the same data, achieving 98.73% accuracy for classifying five cell cycle phases. 4. **Disease Progression Reconstruction**: The network reconstructs the progression of diabetic retinopathy, ordered from healthy to severe, without explicit ordering information. 5. **Speed and Practical Applications**: The deep learning model is significantly faster than traditional methods, allowing for real-time analysis in imaging flow cytometry, which can process up to 1000 cells per second. The authors conclude that deep learning is a powerful tool for understanding continuous biological processes and can be applied to a wide range of image data, provided there are enough samples available. The method's flexibility and speed make it particularly useful for high-throughput applications in biology and medicine.