Preliminary evaluation of an automated autoencoder-UNet pipeline for chemical image segmentation and compression with reference to serial ground truth pathology
The rapid advancement of imaging technologies in pathology has ushered in an era of data-intensive diagnostic workflows, generating large volumes of data that demand sophisticated segmentation and compression techniques. Chemical imaging approaches offer an all-digital objective approach to pathological analysis, though image segmentation is required for efficient computation. Convolutional autoen