AI-Cancer Annotation and Recognition Evolution (AI-CARE): Transforming Pathology
Disciplines
Artificial Intelligence and Robotics | Bioimaging and Biomedical Optics | Computer Sciences | Medical Cell Biology | Medical Pathology
Abstract (300 words maximum)
The meticulous task of identifying and annotating cancer cells in medical images represents a significant challenge in pathology. Trained pathologists face a demanding workload, as they must meticulously examine hundreds, if not thousands, of cells in a single patient's sample, identifying subtle morphological differences that distinguish cancerous cells from healthy ones. This process is not only time-consuming but also prone to human error, given the strenuous nature of the work and the nuanced variability among cancer cells. Addressing this issue, integrating advanced AI systems, such as GPT-4, into the diagnostic workflow presents a promising solution. The research focuses on developing an AI assistant trained to recognize the intricate patterns and characteristics of cancerous cells, such as abnormal cell membrane structures and enlarged nuclei, by analyzing a comprehensive dataset that includes annotated cancerous cells, unannotated cancerous cells, and healthy cells. The potential of GPT-4 to process and interpret medical images with high precision can significantly augment the pathologists' efforts, reducing the time required for diagnosis and increasing the accuracy of cancer detection. By automating the annotation process, such a tool can provide early detection and help pathologists focus on more complex cases and decision-making aspects of patient care, enhancing overall healthcare delivery. However, cancer's heterogeneity poses a significant challenge to developing an effective AI diagnostic tool. Cancer cells exhibit a wide range of variations in appearance and behavior, necessitating a model that can adapt to and accurately identify these diverse characteristics. Focusing on specific types of cancer, such as those affecting breast tissue, permits refining of the model's accuracy in more controlled contexts. Preliminary efforts and results leveraging OpenAI's GPT-4 vision capabilities have shown significant promise in classifying objects and deciphering image content. Further advancements are underway towards autonomous annotation, aiming to revolutionize this intricate process.
Academic department under which the project should be listed
SPCEET - Robotics and Mechatronics Engineering
Primary Investigator (PI) Name
Razvan Voicu
AI-Cancer Annotation and Recognition Evolution (AI-CARE): Transforming Pathology
The meticulous task of identifying and annotating cancer cells in medical images represents a significant challenge in pathology. Trained pathologists face a demanding workload, as they must meticulously examine hundreds, if not thousands, of cells in a single patient's sample, identifying subtle morphological differences that distinguish cancerous cells from healthy ones. This process is not only time-consuming but also prone to human error, given the strenuous nature of the work and the nuanced variability among cancer cells. Addressing this issue, integrating advanced AI systems, such as GPT-4, into the diagnostic workflow presents a promising solution. The research focuses on developing an AI assistant trained to recognize the intricate patterns and characteristics of cancerous cells, such as abnormal cell membrane structures and enlarged nuclei, by analyzing a comprehensive dataset that includes annotated cancerous cells, unannotated cancerous cells, and healthy cells. The potential of GPT-4 to process and interpret medical images with high precision can significantly augment the pathologists' efforts, reducing the time required for diagnosis and increasing the accuracy of cancer detection. By automating the annotation process, such a tool can provide early detection and help pathologists focus on more complex cases and decision-making aspects of patient care, enhancing overall healthcare delivery. However, cancer's heterogeneity poses a significant challenge to developing an effective AI diagnostic tool. Cancer cells exhibit a wide range of variations in appearance and behavior, necessitating a model that can adapt to and accurately identify these diverse characteristics. Focusing on specific types of cancer, such as those affecting breast tissue, permits refining of the model's accuracy in more controlled contexts. Preliminary efforts and results leveraging OpenAI's GPT-4 vision capabilities have shown significant promise in classifying objects and deciphering image content. Further advancements are underway towards autonomous annotation, aiming to revolutionize this intricate process.