Dr.-Ing. · University Hospital Erlangen

Dr.-Ing. Luis Carlos
Rivera Monroy

Postdoctoral Researcher · AI-PREDICT

Department of Stem Cell Biology, University Hospital Erlangen · Erlangen, Germany

I develop graph-based deep learning methods for computational pathology and medical image analysis, with a focus on multimodal histopathology, whole-slide imaging, and GNNs. My work spans neuro-oncology, dermatology, and stroke lesion analysis, combining Transformers, diffusion models, and cell-graph representations to tackle challenging problems in clinical AI. I currently work on AI-PREDICT, integrating multimodal data to predict IBD-PD comorbidity progression.

Scholar GitHub Lab Website CV Email
Luis Carlos Rivera Monroy

PhD Research

Doctoral thesis

Defended 22 June 2026 · Pattern Recognition Lab, FAU · 2020–2026
Interconnected Pathology: A Graph-Based Framework for Analyzing Multimodal Histopathology Samples
Supervised by Prof. Andreas Maier · Pattern Recognition Lab, FAU Erlangen-Nürnberg

Competitive benchmarks

Challenge participations

🥇
BraTS Pathology Challenge, 1st Place
MICCAI 2025 · Brain tumor sub-region classification from whole-slide pathology images. Top-ranked team among all international participants.
🧠
BraTS Challenge
MICCAI 2017 & 2018 · Brain Tumor Segmentation challenge. Co-author on the landmark benchmarking paper (2,771 citations). Tasks: segmentation, survival prediction, progression assessment.
💉
ISLES Challenge
MICCAI 2016 & 2017 · Ischemic Stroke Lesion Segmentation. Benchmarking lesion outcome prediction from multispectral MRI. Published in Frontiers in Neurology (217 citations).

Career

Experience

2026 – present
Postdoctoral Researcher
Department of Stem Cell Biology, University Hospital Erlangen
Erlangen, Germany
AI-PREDICT · AI-driven Multimodal Data Integration for Predicting IBD-PD Comorbidity Progression
2024 – 2026
Senior ML Scientist
Mira Vision Microscopy

Focus areas

Research interests

🔬
Computational Pathology
Deep learning on whole-slide images for tumor grading, classification, and multi-stained tissue analysis.
🕸️
Graph Neural Networks
Cell-graph representations for multi-level cancer profiling and radiomic/genomic feature integration.
🧠
Brain Tumor Segmentation
MRI-based glioma segmentation and histopathology sub-region classification with foundation models.
💉
Stroke Lesion Analysis
Ischemic stroke lesion outcome prediction from multispectral MRI sequences.
🌊
Diffusion Models
Graph-aided diffusion models for precision segmentation of challenging tumor regions in MRI.
🎭
Multimodal Learning
Integrating imaging, omics, and clinical data with Transformers, CNNs, and GNNs for holistic cancer profiling.

Background

Education

2020 – 2026
Dr.-Ing. in Computer Science
Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg
Erlangen, Germany
Supervised by Prof. Andreas Maier · Defended 22 June 2026
2017 – 2020
M.Sc. in Medical Engineering
Friedrich-Alexander-Universität Erlangen-Nürnberg
Erlangen, Germany
2013 – 2017
B.Eng. in Biomedical Engineering
Universidad de los Andes
Bogotá, Colombia
Member of the Biomedical Computer Vision Group (BCV) · Supervised by Prof. Pablo Arbeláez

Selected works

Publications

2026
Virchows ArchivJournal
Vision transformer-based diagnosis of psoriasis and eczema in whole-slide histology
L. Rivera Monroy, A. Petzold, M. Sari, K. Breininger, A. Maier, J. Vera-González, et al.
DOI
2025
MICCAI 2025🥇 BraTS Pathology Winner
Patch-Level Brain Tumor Sub-region Classification Using Foundation Models Under Long-Tailed Data Distributions
L. Rivera Monroy, M. Mayr, L. Mill, H. Köstler, A. Maier
DOI
ISMRM 2025
Graph-Aided Diffusion Models for Precision Segmentation of Challenging Tumor Areas in MRI
L. Rivera Monroy, L. Pfaff, T. Wang, N. Kleuser, A. Maier
Abstract
IJCARSJournal
Graph neural networks in multi-stained pathological imaging: extended comparative analysis of radiomic features
L. Rivera Monroy, L. Rist, C. Ostalecki, A. Bauer, J. Vera, K. Breininger, A. Maier
DOI
Virchows ArchivJournal
An artificial intelligence model of whole-slide pathology specimens differentiating cutaneous high-grade squamous proliferations
A. Petzold, A. Wessely, M. Erdmann, S. Schliep, S. Schreml, L. Rivera Monroy, J. Vera, K. Drexler, et al.
DOI
ML for Biomedical ImagingJournal
A COCO-Formatted Instance-Level Dataset for Plasmodium Falciparum Detection in Giemsa-Stained Blood Smears
F. Wilm, L. Rivera Monroy, M. Öttl, L. Mürdter, L. Mill, A. Maier
arXiv
BVM Workshop 2025
Multi-level cancer profiling through joint cell-graph representations (extended abstract)
L. Rivera Monroy, L. Rist, F. Wilm, C. Ostalecki, A. Baur, J. Vera, K. Breininger, A. Maier
DOI
2024
Smart HealthJournal
Multi-level cancer profiling through joint cell-graph representations
L. Rivera Monroy, L. Rist, F. Wilm, C. Ostalecki, A. Baur, J. Vera, K. Breininger, A. Maier
DOI
BVM Workshop
Comparative analysis of radiomic features and gene expression profiles in histopathology data using graph neural networks
L. Rivera Monroy, L. Rist, M. Eberhardt, C. Ostalecki, A. Bauer, J. Vera, K. Breininger, A. Maier
DOI
ISMRM 2024
Focused MRI Segmentation: Leveraging Diffusion Models for Brain Tumor Segmentation in Low-Resolution Areas
L. Rivera Monroy, T. Wang, V. Belagiannis, A. Maier
Abstract
2023
ISBI 2023
Employing graph representations for cell-level characterization of melanoma MELC samples
L. Rivera Monroy, L. Rist, M. Eberhardt, C. Ostalecki, A. Baur, J. Vera, K. Breininger, A. Maier
IEEE
2022
CHASE 2022
Melanoma Segmentation and Classification Employing Imaging and Graph Encodings
L. Rivera Monroy, M. Eberhardt, C. Ostalecki, A. Baur, J. Vera, A. Maier
IEEE
2020
BVM 2020
Multi-Channel Volumetric Neural Network for Knee Cartilage Segmentation in Cone-Beam CT
J. Maier, L. Rivera Monroy, C. Syben, Y. Jeon, J.-H. Choi, M. Hall, M. Levenston, G. Gold, R. Fahrig, A. Maier
DOI
2018
arXiv / NMI2,771 citations
Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BraTS Challenge
S. Bakas, M. Reyes, A. Jakab, ..., L. Rivera Monroy, et al.
arXiv
Frontiers in NeurologyJournal · 217 citations
ISLES 2016 and 2017: Benchmarking ischemic stroke lesion outcome prediction based on multispectral MRI
S. Winzeck, A. Hakim, R. McKinley, ..., L. Rivera Monroy, et al.
DOI
MICCAI Brainlesion Workshop
Brain tumor segmentation and parsing on MRIs using multiresolution neural networks
L. Castillo, L. Daza, L. Rivera Monroy, P. Arbeláez
PDF
2017
MIPAA / SPIE 2017
Volumetric multimodality neural network for brain tumor segmentation
L. Castillo, L. Daza, L. Rivera Monroy, P. Arbeláez
PDF
MICCAI 2017 BraTS
Volumetric multimodality neural network for brain tumor segmentation (BraTS Challenge)
L. Castillo, L. Daza, L. Rivera Monroy, P. Arbeláez
PDF
View full list on Google Scholar

Around the world

Places visited

Countries
22
World coverage
11%
of 195 countries
Continents
4
Americas · Europe · Asia · Oceania
Austria Belgium Bulgaria Colombia Czech Republic France Germany Italy Lithuania Monaco Netherlands Panama Portugal Romania Singapore South Korea Sweden Switzerland Taiwan Thailand United Kingdom United States

Get in touch

Contact

I'm happy to discuss research collaborations, questions about my work, or opportunities in computational pathology and medical AI. Feel free to reach out by email or connect through any of the platforms below.

luis.rivera@fau.de
Google Scholar GitHub · luiscarm9 LinkedIn · luiscriveram ORCID · 0000-0001-8232-8920 Pattern Recognition Lab · FAU