A million-pixel image might only have one annotation from a professional pathologist, posing a significant challenge for all AI models. Our team has designed the world＇s first weakly supervised multiple instance AI learning framework. This framework can analyze million-pixel pathology slides to predict gene expression and prognosis in colorectal cancer patients. Additionally, it has been successfully validated across patient datasets from multiple generational cohorts.
A more significant challenge arises as each complete million-pixel digital pathology slide image often has only one set of annotated answers from a physician, which cannot be applied to all the smaller segmented images. This issue is exactly what top AI experts worldwide, NVIDIA, and our team aim to solve.

Our research team has developed innovative AI algorithms to address this problem. We designed a novel attention-based multiple instance learning model embedded in a convolutional neural network, overcoming the limitations of traditional machine learning methods while effectively highlighting lesion features. This pioneering weakly supervised AI model can automatically analyze and accurately integrate the best prediction after processing a large volume of image data.

Furthermore, our unique AI model enables computers to recognize gene features that pathologists cannot detect with the naked eyes. If successfully implemented in clinical practice, this achievement could reduce the workload of physicians＇ manual annotations by at least 75%. Remarkably, we developed the new deep learning model with an accuracy of up to 99.38%. This novel AI algorithm framework not only identifies specific gene expressions from million-pixel digital pathology images of colorectal cancer but also performs prognostic prediction tasks for patients. Additionally, the model＇s performance has been validated across different cohort study.