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publications (hide)12
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1Wie bereit sind Studierende für die Nutzung von KI-Technologien? Eine Annäherung an die KI-Readiness Studierender im Kontext des Projektes "tech4comp"
J. Riedel, and J. Kleppsch. (2021)
a month ago by @scadsfct
show all tags
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
HumanBewertung,EmpiricalMediennutzung,Lernprozess,Untersuchung,UseActivities),undQualitativeHochschullehre,institute,intelligence,being,Project,student,Vergleich,Bildungswesen,Projekt,process,Judgment,Medien,researchErziehung,Mensch,Judgement,UtilisationStudent,Forschung,Nachteil,Hochschule,EmpirischeTechnologie,Vorteil,Education,UtilizationAssessment,Intelligenz,Universitymedia,HigherSchul-ArtificialMedieneinsatz,(LearningDeploymentMaleProjects370educationK\"{u}nstlicheTechnology,teaching,lecturing,LearningDigitalestudy,of
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1Model soups improve performance of dermoscopic skin cancer classifiers
R. Maron, A. Hekler, S. Haggenmüller, C. von Kalle, J. Utikal, V. Müller, M. Gaiser, F. Meier, S. Hobelsberger, F. Gellrich and 23 other author(s). Eur. J. Cancer, (September 2022)
2 months ago by @scadsfct
show all tags
Artificial
intelligence;
learning;
Deep
Melanoma;
soups;
Dermatology;
Robustness
Calibration;
Ensembles;
topic_lifescience
Model
Generalisation;
Nevus;
Artificialintelligence;learning;DeepMelanoma;soups;Dermatology;RobustnessCalibration;Ensembles;topic_lifescienceModelGeneralisation;Nevus;
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1Artificial intelligence-based detection of FGFR3 mutational status directly from routine histology in bladder cancer: A possible preselection for molecular testing?
C. Loeffler, N. Ortiz Bruechle, M. Jung, L. Seillier, M. Rose, N. Laleh, R. Knuechel, T. Brinker, C. Trautwein, N. Gaisa and 1 other author(s). Eur. Urol. Focus, 8 (2): 472--479 (March 2022)
2 months ago by @scadsfct
show all tags
Artificial
intelligence;
learning;
therapy
for
Deep
mutations;
Bladder
cancer;
Molecular
fibroblast
receptor
testing
growth
factor
FGFR3
Artificialintelligence;learning;therapyforDeepmutations;Bladdercancer;MolecularfibroblastreceptortestinggrowthfactorFGFR3
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1Artificial intelligence to identify genetic alterations in conventional histopathology
D. Cifci, S. Foersch, and J. Kather. J. Pathol., 257 (4): 430--444 (July 2022)
2 months ago by @scadsfct
show all tags
image
artificial
oncology
intelligence;
precision
analysis;
biomarker;
imageartificialoncologyintelligence;precisionanalysis;biomarker;
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1Explainable artificial intelligence in skin cancer recognition: A systematic review
K. Hauser, A. Kurz, S. Haggenmüller, R. Maron, C. von Kalle, J. Utikal, F. Meier, S. Hobelsberger, F. Gellrich, M. Sergon and 23 other author(s). Eur. J. Cancer, (May 2022)
2 months ago by @scadsfct
show all tags
Systematic
Artificial
Dermatology;
intelligence;
topic_lifescience
Man-machine
review
Skin
neoplasms;
systems;
SystematicArtificialDermatology;intelligence;topic_lifescienceMan-machinereviewSkinneoplasms;systems;
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1Deep learning identifies inflamed fat as a risk factor for lymph node metastasis in early colorectal cancer
S. Brockmoeller, A. Echle, N. Ghaffari Laleh, S. Eiholm, M. Malmstrøm, T. Plato Kuhlmann, K. Levic, H. Grabsch, N. West, O. Saldanha and 7 other author(s). J. Pathol., 256 (3): 269--281 (March 2022)
2 months ago by @scadsfct
show all tags
metastasis;
inflamed
intelligence;
deep
learning;
bowel
predictive
LNM
adipose
pT2
pT1
digital
biomarker;
cancer;
artificial
AI;
pathology;
colorectal
and
prediction
new
tissue;
early
metastasis;inflamedintelligence;deeplearning;bowelpredictiveLNMadiposepT2pT1digitalbiomarker;cancer;artificialAI;pathology;colorectalandpredictionnewtissue;early
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1Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology
N. Ghaffari Laleh, H. Muti, C. Loeffler, A. Echle, O. Saldanha, F. Mahmood, M. Lu, C. Trautwein, R. Langer, B. Dislich and 13 other author(s). Med. Image Anal., 79 (102474): 102474 (July 2022)
2 months ago by @scadsfct
show all tags
Convolutional
Artificial
intelligence;
Multiple-Instance
deep
neural
networks;
learning
Weakly-supervised
Learning;
transformers;
pathology;
Vision
Computational
ConvolutionalArtificialintelligence;Multiple-Instancedeepneuralnetworks;learningWeakly-supervisedLearning;transformers;pathology;VisionComputational
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1Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction
D. Nam, J. Chapiro, V. Paradis, T. Seraphin, and J. Kather. JHEP Rep., 4 (4): 100443 (April 2022)
2 months ago by @scadsfct
show all tags
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
adisease;HCC,orintelligence;livermultivariablenon-alcoholicmultimodalML,artificialWSIs,images;predictionsupportintegrationTACE,DICOM,AI,network;deepneuralDigitalDiagnosis;MVI,transarterialfattymicrovascularconvolutionalinlearning;imaging;Communicationsinvasion;NAFLD,chemoembolisation;hepatocellularReportingsteatohepatitis;TransparentIndividualslidemachinePrognosisArtificialdataforNASH,wholeMedicine;TRIPOD,ofCNN,andImagingcarcinoma;diagnosticsystem;model
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1Weakly supervised annotation-free cancer detection and prediction of genotype in routine histopathology
P. Schrammen, N. Ghaffari Laleh, A. Echle, D. Truhn, V. Schulz, T. Brinker, H. Brenner, J. Chang-Claude, E. Alwers, A. Brobeil and 9 other author(s). J. Pathol., 256 (1): 50--60 (January 2022)
2 months ago by @scadsfct
show all tags
Lynch
intelligence;
deep
learning;
digital
instability
cancer;
artificial
syndrome;
pathology;
colorectal
microsatellite
computational
Lynchintelligence;deeplearning;digitalinstabilitycancer;artificialsyndrome;pathology;colorectalmicrosatellitecomputational
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1The future of artificial intelligence in digital pathology - results of a survey across stakeholder groups
C. Heinz, A. Echle, S. Foersch, A. Bychkov, and J. Kather. Histopathology, 80 (7): 1121--1127 (June 2022)
2 months ago by @scadsfct
show all tags
artificial
intelligence;
pathology;
survey
digital
artificialintelligence;pathology;surveydigital
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1Artificial intelligence predicts immune and inflammatory gene signatures directly from hepatocellular carcinoma histology
Q. Zeng, C. Klein, S. Caruso, P. Maille, N. Laleh, D. Sommacale, A. Laurent, G. Amaddeo, D. Gentien, A. Rapinat and 10 other author(s). J. Hepatol., 77 (1): 116--127 (July 2022)
2 months ago by @scadsfct
show all tags
image
artificial
immune
intelligence;
pathology;
gene
deep
learning;
signatures;
slide
whole
imageartificialimmuneintelligence;pathology;genedeeplearning;signatures;slidewhole
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1How Does Explainability Look in Hybrid User Interfaces?
J. Méndez, M. Satkowski, and R. Rzayev. 2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), page 251--256. (2023)
2 months ago by @scadsfct
show all tags
artificial
Visual
sciences;Artificial
intelligence;hybrid
interfaces;Complexity
analytics;Medical
services;Games;User
intelligence;explainable
reality
theory;Behavioral
user
interfaces;mixed
artificialVisualsciences;Artificialintelligence;hybridinterfaces;Complexityanalytics;Medicalservices;Games;Userintelligence;explainablerealitytheory;Behavioraluserinterfaces;mixed
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publications (hide)12
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1Wie bereit sind Studierende für die Nutzung von KI-Technologien? Eine Annäherung an die KI-Readiness Studierender im Kontext des Projektes "tech4comp"

1Model soups improve performance of dermoscopic skin cancer classifiers

1Artificial intelligence-based detection of FGFR3 mutational status directly from routine histology in bladder cancer: A possible preselection for molecular testing?

1Artificial intelligence to identify genetic alterations in conventional histopathology

1Explainable artificial intelligence in skin cancer recognition: A systematic review

1Deep learning identifies inflamed fat as a risk factor for lymph node metastasis in early colorectal cancer

1Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology

1Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction

1Weakly supervised annotation-free cancer detection and prediction of genotype in routine histopathology

1The future of artificial intelligence in digital pathology - results of a survey across stakeholder groups

1Artificial intelligence predicts immune and inflammatory gene signatures directly from hepatocellular carcinoma histology

1How Does Explainability Look in Hybrid User Interfaces?

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