**11. Innovating through challenges**

The challenges that have crossed the field of AI and computer vision in healthcare have also promoted the search for solutions. This search has sparked ideas and achieved some interesting proposals that are slowly being incorporated into daily practice. To begin with, the problem of generating labels in WSIs gave rise to a new technique called multiple instance learning (MIL). This technique uses as labels only the diagnosis of the patient (which is usually available in EHRs). Thanks to this new approach, a group managed to analyze 44,732 WSIs without any kind of data curation, incredibly speeding up project times [132]. As we also mentioned, the variability between samples from different hospitals is a problem that threatens the creation of large datasets. One of the solutions to this problem was the creation of stain normalization. This is a method that in one of its variants uses autoencoders and allows to standardize of the color distribution in the images, using another image as a template [133]. Thanks to this method, it is possible to have more homogeneous images, even if they come from different laboratories. Regarding the weight of the WSIs, generally, only a small part of the image is used by the deep learning models for the task they perform. For example, as the image passes through the successive layers of a CNN, the information is reduced. In the last layers, only the essential information remains that will complete the task with the least possible error. Using this principle, one group created the concept of neural compression. Basically what this group proposed is to create abstract representations of the WSI images after passing through successive steps in a convolutional network. In this way, noise is removed at each step and only a small, compressed representation remains [134]. This concept would help store WSIs

more efficiently with only the information needed for the task. Finally, to provide the greatest privacy protection to patients and also speed up data exchange processes, blockchain networks and interplanetary file system (IPFS) can be used. In this way, the information is decentralized, which reduces the risk of data leakage. In addition, the different hospitals participating in the study can provide the files, which can be fragmented and hashed according to IFPS. The entire process would be governed by one or several smart contracts, which would ensure that only authorized nodes contribute data or extract data. Smart contracts may also contain portions of sensitive information, which would eliminate the need for human interaction and the possible breach of confidentiality [135–137].
