Enhancing Content-Based Histopathology Image Retrieval Using QR Code Representation

The growing field of Content-Based Medical Image Retrieval (CBMIR) plays an integral role in the diagnosis and treatment plan of numerous diseases, including cancer. However, the effective representation of gigapixel medical images via a large number of extracted features remains a challenging task, crucially influencing other processes in a digital workflow. In this paper, we propose a novel QR code representation strategy to enhance retrieval performance. Unlike the traditional one-dimensional binary representation methods (i.e., barcodes), this 2D approach captures more intricate and informative patterns from the features by differentiating each pair of features resulting in a 2D binary vector. We delve into three distinct QR code generation strategies, namely, the Thresholding QR, the MinMax QR, and the Hybrid QR, each offering unique strengths. Our experiments on representing whole slide images of the Cancer Genome Atlas (TCGA) dataset reveal that while the Hybrid QR tends to provide balanced performance, there are instances where the other two methods outshine. Even though this approach requires more memory usage, the considerable enhancement in accuracy justifies this trade-off; obviously, in medical applications, accuracy holds the highest priority. Hence, the findings indicate that QR codes can effectively improve the performance of CBMIR systems by not only accelerating the retrieval process but also increasing the accuracy of image retrieval, leading to potentially more accurate diagnoses and treatment planning.