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 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]
Artificial intelligence predicts immune and inflammatory gene signatures directly from hepatocellular carcinoma histology. J. Hepatol., (77)1:116--127, Elsevier BV, Juli 2022. [PUMA: image artificial immune intelligence; pathology; gene deep learning; signatures; slide whole]
Artificial intelligence to identify genetic alterations in conventional histopathology. J. Pathol., (257)4:430--444, Wiley, Juli 2022. [PUMA: image artificial oncology intelligence; precision analysis; biomarker;]
Artificial intelligence-based detection of FGFR3 mutational status directly from routine histology in bladder cancer: A possible preselection for molecular testing?. Eur. Urol. Focus, (8)2:472--479, Elsevier BV, März 2022. [PUMA: Artificial intelligence; learning; therapy for Deep mutations; Bladder cancer; Molecular fibroblast receptor testing growth factor FGFR3]
Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology. Med. Image Anal., (79)102474:102474, Elsevier BV, Juli 2022. [PUMA: Convolutional Artificial intelligence; Multiple-Instance deep neural networks; learning Weakly-supervised Learning; transformers; pathology; Vision Computational]
Deep learning identifies inflamed fat as a risk factor for lymph node metastasis in early colorectal cancer. J. Pathol., (256)3:269--281, Wiley, März 2022. [PUMA: metastasis; inflamed intelligence; deep learning; bowel predictive LNM adipose pT2 pT1 digital biomarker; cancer; artificial AI; pathology; colorectal and prediction new tissue; early]
Deep reinforcement learning with artificial microswimmers. Emerging Topics in Artificial Intelligence (ETAI) 2022, (12204):104--110, 2022. [PUMA: microswimmers artificial learning reinforcement Deep]
Explainable artificial intelligence in skin cancer recognition: A systematic review. Eur. J. Cancer, (167):54--69, Elsevier BV, Mai 2022. [PUMA: Systematic Artificial Dermatology; intelligence; topic_lifescience Man-machine review Skin neoplasms; systems;]
How Does Explainability Look in Hybrid User Interfaces?. 2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), 251--256, 2023. [PUMA: artificial Visual sciences;Artificial intelligence;hybrid interfaces;Complexity analytics;Medical services;Games;User intelligence;explainable reality theory;Behavioral user interfaces;mixed]
Model soups improve performance of dermoscopic skin cancer classifiers. Eur. J. Cancer, (173):307--316, Elsevier BV, September 2022. [PUMA: Artificial intelligence; learning; Deep Melanoma; soups; Dermatology; Robustness Calibration; Ensembles; topic_lifescience Model Generalisation; Nevus;]
The future of artificial intelligence in digital pathology - results of a survey across stakeholder groups. Histopathology, (80)7:1121--1127, Wiley, Juni 2022. [PUMA: artificial intelligence; pathology; survey digital]
Weakly supervised annotation-free cancer detection and prediction of genotype in routine histopathology. J. Pathol., (256)1:50--60, Wiley, Januar 2022. [PUMA: Lynch intelligence; deep learning; digital instability cancer; artificial syndrome; pathology; colorectal microsatellite computational]
Wie bereit sind Studierende für die Nutzung von KI-Technologien? Eine Annäherung an die KI-Readiness Studierender im Kontext des Projektes "tech4comp". Waxmann : Münster ; New York, 2021. [PUMA: Human Bewertung, Empirical Mediennutzung, Lernprozess, Untersuchung, Use Activities), und Qualitative Hochschullehre, institute, intelligence, being, Project, student, Vergleich, Bildungswesen, Projekt, process, Judgment, Medien, research Erziehung, Mensch, Judgement, Utilisation Student, Forschung, Nachteil, Hochschule, Empirische Technologie, Vorteil, Education, Utilization Assessment, Intelligenz, University media, Higher Schul- Artificial Medieneinsatz, (Learning Deployment Male Projects 370 education K\"{u}nstliche Technology, teaching, lecturing, Learning Digitale study, of] URL