HIQ: One-Shot Network Quantization for Histopathological Image Classification

To deploy neural networks on clinical edge devices, quantization is the most commonly used method to compress the models, which requires a calibration set of hundreds of real images. However, due to privacy concerns, the scarcity of private histopathological images hinders the application of quantization. To address this issue, we develop HIQ, a novel one-shot quantization framework for histopathological image classification networks, which requires only one real image per class for calibration. To compensate for data scarcity, sample BNS alignment is introduced to generate synthetic images with similar distribution to the real ones. To improve the diversity of synthetic images, fine-grained diversity enhancement that provides fine-grained enhancement intensity for different classes and network layers is proposed, based on the observation of the class-wise and layer-wise fine-grained data. Finally, the asymptotic enhancement strategy is highlighted to achieve a trade-off between inter-class distance and intra-class diversity of synthetic images, based on the insight of the smaller inter-class distance of histopathological images than that of natural ones. Extensive experiments on the BRACS dataset show that our method achieves an extremely low accuracy loss even compared to the full precision model in low-bit cases and maintains robustness when missing classes of real images.