2020/10/20 by Pablo Messina, Pablo del Pino, Messina, Pablo +20 · 2 citations
Computer Science · Engineering · Medicine · #AI in cancer detection #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Medical Imaging and Analysis #Radiomics and Machine Learning in Medical Imaging #cs.AI #cs.CL #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.2010.10563
Accepted for publication in ACM CSUR
openalex publication_date 2020/10/20 · arxiv created 2022/01/08 · arxiv updated 2022/01/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Every year physicians face an increasing demand of image-based diagnosis from patients, a problem that can be addressed with recent artificial intelligence methods. In this context, we survey works in the area of automatic report generation from medical images, with emphasis on methods using deep neural networks, with respect to: (1) Datasets, (2) Architecture Design, (3) Explainability and (4) Evaluation Metrics. Our survey identifies interesting developments, but also remaining challenges. Among them, the current evaluation of generated reports is especially weak, since it mostly relies on traditional Natural Language Processing (NLP) metrics, which do not accurately capture medical correctness.