2021/10/26 by Jialun Wu, Wu, Jialun, Zeyu Gao +11 · 1 citation
Computer Science · Medicine · #AI in cancer detection #Artificial Intelligence (cs.AI) #Artificial intelligence #Bioinformatics #Colorectal Cancer Screening and Detection #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Data mining #Data source #FOS: Computer and information sciences #Image (mathematics) #Information retrieval #Medical diagnosis #Medical physics #Medicine #Pathological #Pathology #Personalized medicine #Population #Precision medicine #Radiomics and Machine Learning in Medical Imaging #Similarity (geometry) #cs.AI #cs.CV
paper · pdf · doi:10.48550/arxiv.2110.13677
published in arXiv (Cornell University) (Cornell University) · BIBM 2021 accepted, including 9 pages, 3 figures
arxiv created 2021/10/26 · openalex publication_date 2021/10/26 · arxiv updated 2021/10/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Personalized diagnoses have not been possible due to sear amount of data pathologists have to bear during the day-to-day routine. This lead to the current generalized standards that are being continuously updated as new findings are reported. It is noticeable that these effective standards are developed based on a multi-source heterogeneous data, including whole-slide images and pathology and clinical reports. In this study, we propose a framework that combines pathological images and medical reports to generate a personalized diagnosis result for individual patient. We use nuclei-level image feature similarity and content-based deep learning method to search for a personalized group of population with similar pathological characteristics, extract structured prognostic information from descriptive pathology reports of the similar patient population, and assign importance of different prognostic factors to generate a personalized pathological diagnosis result. We use multi-source heterogeneous data from TCGA (The Cancer Genome Atlas) database. The result demonstrate that our framework matches the performance of pathologists in the diagnosis of renal cell carcinoma. This framework is designed to be generic, thus could be applied for other types of cancer. The weights could provide insights to the known prognostic factors and further guide more precise clinical treatment protocols.