Maximilian Alber

Medicine · Health Sciences

13h-index1.5kcitations48works3.22yr avg
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Research Topics

Radiomics and Machine Learning in Medical Imaging(14), AI in cancer detection(12), Explainable Artificial Intelligence (XAI)(10), Adversarial Robustness in Machine Learning(6), Neural Networks and Applications(5)

Publications48 total

177eP Enhancing precision oncology: Granular H-score prediction from H&E images using deep learning
Annals of Oncology·2025
Towards Robust Foundation Models for Digital Pathology
arXiv (Cornell University)·2025Open Access
Abstract 3351: From bench to bedside: generalizable AI model for ADC biomarker evaluation in NSCLC
Cancer Research·2025
Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charité, and Aignostics
arXiv (Cornell University)·2025· 3 citedOpen Access
1200 Artificial intelligence image models optimizing tumor vs immune cell profiling for accurate biomarker analysis in NSCLC precision drug development
Regular and Young Investigator Award Abstracts·2024Open Access
Leveraging weak complementary labels enhances semantic segmentation of hepatocellular carcinoma and intrahepatic cholangiocarcinoma
Scientific Reports·2024· 2 citedOpen Access
Dissecting AI-based mutation prediction in lung adenocarcinoma: A comprehensive real-world study
European Journal of Cancer·2024· 9 citedOpen Access
AI-based Anomaly Detection for Clinical-Grade Histopathological Diagnostics
arXiv (Cornell University)·2024Open Access
Erklärbare Künstliche Intelligenz in der Pathologie
Forum·2024
Abstract 5222: AI-driven, mIF-based cell-omics reveals spatially resolved cell signature for outcome prediction in NSCLC patients
Cancer Research·2024
Erklärbare Künstliche Intelligenz in der Pathologie
Die Pathologie·2024
RudolfV: A Foundation Model by Pathologists for Pathologists
arXiv (Cornell University)·2024· 24 citedOpen Access
970 Multiplex-immunofluorescence-based spatial characterization of the tumor-microenvironment of a large bicentric clinical non-small cell lung cancer cohort
Regular and Young Investigator Award Abstracts·2023Open Access
1283 A novel, scalable deep learning-based approach to automated quality control of multiplex immunofluorescence images
Regular and Young Investigator Award Abstracts·2023Open Access
1276 Leveraging artificial intelligence (AI) models delineating tumor vs immune cell expression for scalable biomarker analysis of clinical trial samples: a digital image analysis approach for NSCLC
Regular and Young Investigator Award Abstracts·2023Open Access
Toward Explainable Artificial Intelligence for Precision Pathology
Annual Review of Pathology Mechanisms of Disease·2023· 103 citedOpen Access
Abstract 5423: AI powered quantification of mitotic rate in H&E stained tissue detects significant differences between treatment groups of preclinical pancreas cancer xenografts
Cancer Research·2023
Abstract 5441: Cell cycle arrest status predicted from H&E stained images using deep learning
Cancer Research·2023
Leveraging weak complementary labels to improve semantic segmentation of hepatocellular carcinoma and cholangiocarcinoma in H&E-stained slides
arXiv (Cornell University)·2023Open Access
Analysing cerebrospinal fluid with explainable deep learning: From diagnostics to insights
Neuropathology and Applied Neurobiology·2022· 7 citedOpen Access
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Frequent Co-authors

Frederick Klauschen(20), Lukas Ruff(18), Klaus‐Robert Müller(17), Simon Schallenberg(14), Pieter-Jan Kindermans(8), Kristof T. Schütt(8), Sharon Ruane(8), Sven Dähne(7), Jonas Dippel(7), Philipp Jurmeister(7), Philipp Seegerer(7), Gabriel Dernbach(7), David Horst(6), Michael Bockmayr(5), Grégoire Montavon(5), Stephan Tietz(5), Rosemarie Krupar(5), Cornelius Böhm(5), Dumitru Erhan(4), Been Kim(4)