Enhancing Histopathology Image Classification with Neural Photo Finishing

The field of histopathology, which involves analyzing tissue samples to diagnose diseases, relies heavily on the expertise of pathologists. However, even experienced professionals can make errors, especially when dealing with complex procedures like neural photo finishing. Fortunately, artificial intelligence (AI) has emerged as a powerful tool to assist pathologists in this process.

Neural photo finishing is a complicated procedure that demands a second opinion or assistance from AI algorithms. These algorithms can provide significant help through automation or offer a second opinion to pathologists. The goal of using AI in histopathology image classification is to improve the robustness of the classifier and reduce the risk of human error.

One approach to achieving this goal is by utilizing deep neural network models trained on large datasets like PatchCamelyon, which consists of 220,025 images. This dataset allows for the training of a convolutional neural network (CNN), a state-of-the-art classifier that can classify images without relying on manual feature selection. The CNN was trained on 80% of the images and validated on the remaining 20%. The results showed that the adaptive-based optimizers achieved the highest AUC values, except for AdaGrad, which achieved the lowest.

The main aim of this research is to compare six different first-order stochastic gradient-based optimizers to select the best one for the PatchCamelyon dataset. The developed algorithm receives a histopathology image as input and produces a percentage of cancer presence. This classification process requires an experienced physician with years of experience to accurately classify histopathology images.

In conclusion, neural photo finishing has the potential to revolutionize the field of histopathology by providing a reliable and accurate tool for pathologists. By incorporating AI algorithms into the process, we can improve the robustness of the classifier and reduce errors. As research continues to evolve, we can expect even more advanced applications of neural photo finishing in the future.