Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction. JHEP Rep., (4)4:100443, Elsevier BV, April 2022. [PUMA: a disease; HCC, or intelligence; liver multivariable non-alcoholic multimodal ML, artificial WSIs, images; prediction support integration TACE, DICOM, AI, network; deep neural Digital Diagnosis; MVI, transarterial fatty topic_lifescience microvascular convolutional in learning; imaging; Communications invasion; NAFLD, chemoembolisation; hepatocellular Reporting steatohepatitis; Transparent Individual slide machine Prognosis Artificial data for NASH, whole Medicine; TRIPOD, of CNN, and Imaging carcinoma; diagnostic system; model]
Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology. Med. Image Anal., (79)102474:102474, Elsevier BV, July 2022. [PUMA: Convolutional Artificial intelligence; Multiple-Instance deep neural networks; learning Weakly-supervised Learning; transformers; pathology; topic_lifescience Vision Computational]
BiCAE -- A Bimodal Convolutional Autoencoder for Seed Purity Testing. In Albert Bifet, Tomas Krilavicius, Ioanna Miliou, and Slawomir Nowaczyk (Eds.), Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track, 447--462, Springer Nature Switzerland, Cham, 2024. [PUMA: Convolutional Testing Autoencoder Seed Purity Bimodal Zno BiCAE]
Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning. ECCM21 - Proceedings of the 21st European Conference on Composite Materials, (3):1252--1259, European Society for Composite Materials (ESCM), Jul 2, 2024. [PUMA: Convolutional FIS_scads Fiber area_architectures neural plastics rate dependency, reinforced Direct Strain Machine learning, networks, identification, parameter topic_engineering] URL
Impact of pre- and post-processing steps for supervised classification of colorectal cancer in hyperspectral images. Cancers (Basel), (15)7April 2023. [PUMA: convolutional learning; cancer networks; imaging; classification; pre-processing post-processing; cancer; filter; colorectal median machine hyperspectral topic_lifescience]
Integration of deep learning-based image analysis and genomic data in cancer pathology: A systematic review. Eur. J. Cancer, (160):80--91, Elsevier BV, January 2022. [PUMA: Convolutional Biomarker Multimodal neural networks; Omics identification; topic_lifescience Cancer; fusion;]
Predicting years with extremely low gross primary production from daily weather data using Convolutional Neural Networks. Environmental Data Science, (1):e2, 2022. [PUMA: Convolutional Networks Predicting low gross production data primary Neural weather]
Structural damage identification of composite rotors based on fully connected neural networks and convolutional neural networks. Sensors (Basel), (21)6:2005, MDPI AG, March 2021. [PUMA: convolutional (SHM) monitoring learning; neural networks; rotors; health dense composites; connected structural composite machine fully]