Kernel-Guided Transformer-CNN Network for Low-Cost Pathological Whole Slide Imaging: Hardware-Algorithm Synergy

High-cost commercial digital pathology systems limit their accessibility in resource-constrained healthcare settings. While low-cost alternatives with only XY-axis stages reduce expenses, they often produce blurry images due to defocus (blur induced by hardware constraints leading to poor focus). We present a hardware-algorithm co-design solution that combines simplified, low-cost hardware with targeted intelligent software to address this problem. Our method uses a basic XY-axis scanning system and a three-step automated pipeline: a lightweight model first classifies defocus levels, then estimates “blur kernels” (mathematical representations of blur), and finally uses our Kernel-Guided Transformer-CNN Network (KGTCN) to restore clarity. Tests on clinical datasets show the system generates diagnostic-grade images—reaching up to 38.76 dB PSNR (image sharpness index) and 0.968 SSIM (similarity to clear images), even 35.53 dB/0.941 for severely blurred ones—while processing 43.45 images per second (FPS) with modest resources. Our Global Optimization Registration Algorithm (GORA) also significantly reduces stitching alignment errors, eliminating cumulative misalignments at tile boundaries. This “simplified hardware + targeted software” approach enables sub-$10k systems to produce high-quality Whole Slide Images (WSI), advancing accessible digital pathology in underserved regions