Publications 2024

Cancer Research, march 2024 - Poster

AI-driven, mIF-based cell-omics reveals spatially resolved cell signature for outcome prediction in NSCLC patients

Simon Schallenberg, Gabriel Dernbach, Sharon Ruane, et al

AACR Cancer Research, march 2024 - Poster

TTF-1 status in early-stage lung adenocarcinoma is an independent predictor of relapse and survival superior to tumor grading

Simon Schallenberg, Gabriel Dernbach, Mihnea P Dragomir, et al

Cancer Research, march 2024 - Poster

Neoadjuvant chemotherapy and radiation shape the local and regional adaptive antitumor immune response in lung squamous cell carcinomas

Rosemarie Krupar, Justin Nieder, Johannes Braegelmann, et al

Pathologie, february 2024, Article

Explainable artificial intelligence in pathology

Frederick Klauschen, Jonas Dippel, Philipp Keyl, et al

arXiv, january 2024 - paper

RudolfV: A Foundation Model by Pathologists for Pathologists

Jonas Dippel, Barbara Feulner, Tobias Winterhoff, et al

European Journal of Cancer, january 2024 - Paper

TTF-1 status in early-stage lung adenocarcinoma is an independent predictor of relapse and survival superior to tumor grading

Simon Schallenberg, Gabriel Dernbach, Mihnea P Dragomir, et al

Annual Review of Pathology: Mechanisms of Disease, january 2024 - paper

Toward explainable artificial intelligence for precision pathology

Frederick Klauschen, Jonas Dippel, Philipp Keyl, et al

Publications 2023

Digital Pathology & AI Congress, december 2023 - poster

Maximize Biomarker Information from Tissue Samples: AI/Machine Learning for Combined Spatial Analysis of Multiplex IF and H&E

Roman Schulte-Sasse, Beatriz Perez, Deepti Agrawal, et al

Journal for ImmunoTherapy of Cancer, november 2023 - poster

Multiplex-immunofluorescence-based spatial characterization of the tumor-microenvironment of a large bicentric clinical non-small cell lung cancer cohort

Simon Schallenberg, Gabriel Dernbach, Sharon Ruane, et al

Journal for ImmunoTherapy of Cancer, november 2023 - poster

A novel, scalable deep learning-based approach to automated quality control of multiplex immunofluorescence images

Annika F Fink, Roman Schulte-Sasse, Martin Bauw, et al

Journal for ImmunoTherapy of Cancer, november 2023 - poster

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

Cornelius Böhm, Katja Lingelbach, Blanca Pablos, et al

Toxicologic Pathology, september 2023, poster

AI-based testicular staging in dogs from nonclinical toxicity studies

Leonie Herkommer, Maximilian Gottschalk, Miriam Hägele, et al

Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track, june 2023 - paper

DiffInfinite: Large Mask-Image Synthesis via Parallel Random Patch Diffusion in Histopathology

Marco Aversa, Gabriel Nobis, Miriam Hägele, et al

bioRxiv, may 2023 - paper

High-resolution molecular atlas of a lung tumor in 3D

Tancredi Massimo Pentimalli, Simon Schallenberg, Daniel Leon-Perinan, et al

The American Association for Cancer Research, april 2023 - poster

AI powered quantification of mitotic rate in H&E stained tissue detects significant differences between treatment groups of preclinical pancreas cancer xenografts

Sharon Ruane, Lukas Ruff, Brian Reichholf, et al

The American Association for Cancer Research, april 2023 - poster

Cell cycle arrest status predicted from H&E stained images using deep learning

Christina Aigner, Brian Reichholf, Maxime Emschwiller, et al

arxiv, february 2023 - paper

Leveraging weak complementary labels to improve semantic segmentation of hepatocellular carcinoma and cholangiocarcinoma in H&E-stained slides

Miriam Hägele, Johannes Eschrich, Lukas Ruff, et al

Journal of Pathology Informatics, january 2023 - paper

Imaging bridges pathology and radiology

Hansmann Martin-Leo, Klauschen Frederick, Samek Wojciech, et al

Publications 2022

Transactions on Machine Learning Research, november 2022 - paper

Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images

Philipp Liznerski, Lukas Ruff, Robert A Vandermeulen, et al

Pathologie Heidelb, november 2022 - review paper

The tumor microenvironment-relay station for prognosis and therapy response

Rosemarie Krupar

VIRCHOWS ARCHIV, september 2022 - talk

Extensive spatial characterisation of the tumour-microenvironment in a large clinical non-small cell lung cancer cohort and correlation with clinical and pathological parameters

Sharon Ruane, Simon Schallenberg, Cornelius Böhm, et al

Annals of Oncology, september 2022 - talk

68MO Generalization of a deep learning model for HER2 status predictions on H&E-stained whole slide images derived from 3 neoadjuvant clinical studies

Miriam Hägele, Klaus-Robert Müller, Carsten Denkert, et al

Seminars in cancer biology, september 2022 - paper

Artificial intelligence and pathology: From principles to practice and future applications in histomorphology and molecular profiling

Albrecht Stenzinger, Maximilian Alber, Michael Allgäuer, et al

Journal of Fungi, august 2022 - paper

Deep learning assisted diagnosis of onychomycosis on whole-slide images

Philipp Jansen, Adelaida Creosteanu, Viktor Matyas, et al

Cancer Research, june 2022 - poster

Immunohistochemistry-informed AI systems for improved characterization of tumor-microenvironment in clinical non-small cell lung cancer H&E samples

Thomas Mrowiec, Sharon Ruane, Simon Schallenberg, et al

The Journal of Pathology, april 2022 - paper

Machine learning models predict the primary sites of head and neck squamous cell carcinoma metastases based on DNA methylation

Maximilian Leitheiser, David Capper, Philipp Seegerer, et al

GRUR International, april 2022 - paper

Clarifying Assumptions About Artificial Intelligence Before Revolutionising Patent Law

Daria Kim, Maximilian Alber, Man Wai Kwok, et al

Publications 2021

Max Planck Institute for Innovation & Competition Research Paper, august 2021 - paper

Ten Assumptions About Artificial Intelligence That Can Mislead Patent Law Analysis

Daria Kim, Maximilian Alber, Man Wai Kwok, et al

Pattern Recognition, july 2021 - paper

Pruning by explaining: A novel criterion for deep neural network pruning

Seul-Ki Yeom, Philipp Seegerer, Sebastian Lapuschkin, et al

ICML 2021 Workshop on Uncertainty and Robustness in Deep Learning, july 2021 - paper

Rethinking Assumptions in Deep Anomaly Detection

Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, et al

Nature Machine Intelligence, march 2021 - paper

Morphological and molecular breast cancer profiling through explainable machine learning

Alexander Binder, Michael Bockmayr, Miriam Hägele, et al