QuantU-Net: Efficient Wearable Medical Imaging Using Bitwidth as a Trainable Parameter

Medical image segmentation, particularly tumor segmentation, is a critical task in medical imaging, with U-Net being a widely adopted convolutional neural network (CNN) architecture for this purpose. However, U-Net's high computational and memory requirements pose challenges for deployment on resource-constrained devices such as wearable medical systems. This paper addresses these challenges by introducing QuantU-Net, a quantized version of U-Net optimized for efficient deployment on low-power devices like Field-Programmable Gate Arrays (FPGAs). Using Brevitas, a PyTorch library for quantization-aware training, we quantize the U-Net model, reducing its precision to an average of 4.24 bits while maintaining a validation accuracy of 94.25%, only 1.89% lower than the floating-point baseline. The quantized model achieves an approximately 8x reduction in size, making it suitable for real-time applications in wearable medical devices. We employ a custom loss function that combines Binary Cross-Entropy (BCE) Loss, Dice Loss, and a bitwidth loss function to optimize both segmentation accuracy and the size of the model. Using this custom loss function, we have significantly reduced the training time required to find an optimal combination of bitwidth and accuracy from a hypothetical 6^23 number of training sessions to a single training session. The model's usage of integer arithmetic highlights its potential for deployment on FPGAs and other designated AI accelerator hardware. This work advances the field of medical image segmentation by enabling the deployment of deep learning models on resource-constrained devices, paving the way for real-time, low-power diagnostic solutions in wearable healthcare applications.