An artificial intelligence model of whole-slide pathology specimens differentiating cutaneous high-grade squamous proliferations - Petzold, Anne; Wessely, Anja; Erdmann, Michael; Schliep, Stefan; Schreml, Stephan; Rivera Monroy, Luis Carlos; Vera, Julio; Drexler, Konstantin; Niebel, Dennis; Hayani, Kinan Maurice; Kiesewetter, Franklin; Berking, Carola; et al. (Virchows Archiv, 2025)
Cutaneous squamous cell carcinoma (cSCC) and verruca vulgaris (VV) are skin conditions involving the proliferation of epidermal keratinocytes requiring fundamentally different treatments. Histological evaluation of highly differentiated squamous cell proliferations can be challenging, particularly in small or superficial samples. This study aims to improve diagnostic accuracy using an AI model to distinguish cSCC from VV. We developed a deep-learning model using clustering-constrained attention multiple instance learning (CLAM) to classify hematoxylin and eosin-stained whole-slide images (WSIs) as cSCC or VV. The dataset comprised 289 WSIs (n = 148 cSCC, n = 141 VV). Quality control was ensured through expert review: the training cohort was evaluated by four dermatopathologists, and the evaluation cohort by six additional experts. On the training set, the model achieved an AUROC of 0.99, with an accuracy of 94.9% for cSCC and 91.2% for VV. On the evaluation set, it reached an AUROC of 0.96, and accuracies of 82.4% (cSCC) and 97.4% (VV), similar to the average performance of individual dermatopathologists. We successfully trained and implemented an interpretable deep-learning-based weakly supervised model on WSIs distinguishing cSCC from VV, which could enhance AI-supported diagnostics in the future.
CRIS-Team Scopus Importer:2025-10-03
Keywords: Artificial Intelligence; Carcinoma; Squamous Cell; Pathology; Warts

Brain Tumor segmentation and parsing on MRIs using multiresolution neural networks - Silvana Castillo, Laura; Alexandra Daza, Laura; Rivera, Luis Carlos; Arbelaez, Pablo (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018)
Brain lesion segmentation is a critical application of computer vision to the biomedical image analysis. The difficulty is derived from the great variance between instances, and the high computational cost of processing three dimensional data. We introduce a neural network for brain tumor semantic segmentation that parses their internal structures and is capable of processing volumetric data from multiple MRI modalities simultaneously. As a result, the method is able to learn from small training datasets. We develop an architecture that has four parallel pathways with residual connections. It receives patches from images with different spatial resolutions and analyzes them independently. The results are then combined using fully-connected layers to obtain a semantic segmentation of the brain tumor. We evaluated our method using the 2017 BraTS Challenge dataset, reaching average dice coefficients of 89 %, 88 % and 86 % over the training, validation and test images, respectively.
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Keywords: Brain tumors; Deep learning; Machine learning; MRI; Semantic segmentation

Comparative Analysis of Radiomic Features and Gene Expression Profiles in Histopathology Data using Graph Neural Networks - Rivera Monroy, Luis Carlos; Rist, Leonhard; Eberhardt, Martin; Ostalecki, Christian; Bauer, Andreas; Vera, Julio; Breininger, Katharina; Maier, Andreas (Bildverarbeitung für die Medizin 2024, 2024)
This study leverages graph neural networks to integrate MELC data with Radiomic-extracted features for melanoma classification, focusing on cellwise analysis. It assesses the effectiveness of gene expression profiles and Radiomic features, revealing that Radiomic features, particularly when combined with UMAP for dimensionality reduction, significantly enhance classification performance. Notably, using Radiomics contributes to increased diagnostic accuracy and computational efficiency, as it allows for the extraction of critical data from fewer stains, thereby reducing operational costs. This methodology marks an advancement in computational dermatology for melanoma cell classification, setting the stage for future research and potential developments.<br

Employing Graph Representations for Cell-Level Characterization of Melanoma MELC Samples - Rivera Monroy, Luis Carlos; Rist, Leonhard; Eberhardt, Martin; Ostalecki, Christian; Baur, Andreas; Vera, Julio; Breininger, Katharina; Maier, Andreas (2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), 2023)
Histopathology imaging is crucial for the diagnosis and treatment of skin diseases. For this reason, computer-assisted approaches have gained popularity and shown promising results in tasks such as segmentation and classification of skin disorders. However, collecting essential data and sufficiently high-quality annotations is a challenge. This work describes a pipeline that uses suspected melanoma samples that have been characterized using Multi-Epitope-Ligand Cartography (MELC). This cellular level characterization of the tissue is then represented as a graph and finally used to train a graph neural network. This imaging technology, combined with the methodology proposed in this work, achieves a classification accuracy of 87 %, outperforming existing approaches by 10 %.<br

Graph neural networks in multi-stained pathological imaging: extended comparative analysis of Radiomic features - Rivera Monroy, Luis Carlos; Rist, Leonhard; Ostalecki, Christian; Bauer, Andreas; Vera, Julio; Breininger, Katharina; Maier, Andreas (International Journal of Computer Assisted Radiology and Surgery, 2024)
Purpose This study investigates the application of Radiomic features within graph neural networks (GNNs) for the classification of multiple-epitope-ligand cartography (MELC) pathology samples. It aims to enhance the diagnosis of often misdiagnosed skin diseases such as eczema, lymphoma, and melanoma. The novel contribution lies in integrating Radiomic features with GNNs and comparing their efficacy against traditional multi-stain profiles. Methods We utilized GNNs to process multiple pathological slides as cell-level graphs, comparing their performance with XGBoost and Random Forest classifiers. The analysis included two feature types: multi-stain profiles and Radiomic features. Dimensionality reduction techniques such as UMAP and t-SNE were applied to optimize the feature space, and graph connectivity was based on spatial and feature closeness. Results Integrating Radiomic features into spatially connected graphs significantly improved classification accuracy over traditional models. The application of UMAP further enhanced the performance of GNNs, particularly in classifying diseases with similar pathological features. The GNN model outperformed baseline methods, demonstrating its robustness in handling complex histopathological data. Conclusion Radiomic features processed through GNNs show significant promise for multi-disease classification, improving diagnostic accuracy. This study’s findings suggest that integrating advanced imaging analysis with graph-based modeling can lead to better diagnostic tools. Future research should expand these methods to a wider range of diseases to validate their generalizability and effectivenes
Keywords: Radiomics; Multi-array imaging; Graph neural network; MELC; Histopathological analysis

ISLES 2016 and 2017-benchmarking ischemic stroke lesion outcome prediction based on multispectral MRI - Winzeck, Stefan; Hakim, Arsany; Mckinley, Richard; Pinto, Jose A. A. D. S. R.; Alves, Victor; Silva, Carlos; Pisov, Maxim; Krivov, Egor; Belyaev, Mikhail; Monteiro, Miguel; Oliveira, Arlindo; Choi, Youngwon; et al. (Frontiers in Neurology, 2018)
Performance of models highly depend not only on the used algorithm but also the data set it was applied to. This makes the comparison of newly developed tools to previously published approaches difficult. Either researchers need to implement others' algorithms first, to establish an adequate benchmark on their data, or a direct comparison of new and old techniques is infeasible. The Ischemic Stroke Lesion Segmentation (ISLES) challenge, which has ran now consecutively for 3 years, aims to address this problem of comparability. ISLES 2016 and 2017 focused on lesion outcome prediction after ischemic stroke: By providing a uniformly pre-processed data set, researchers from all over the world could apply their algorithm directly. A total of nine teams participated in ISLES 2015, and 15 teams participated in ISLES 2016. Their performance was evaluated in a fair and transparent way to identify the state-of-the-art among all submissions. Top ranked teams almost always employed deep learning tools, which were predominately convolutional neural networks (CNNs). Despite the great efforts, lesion outcome prediction persists challenging. The annotated data set remains publicly available and new approaches can be compared directly via the online evaluation system, serving as a continuing benchmark (www.isles-challenge.org).
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Keywords: Benchmarking; Datasets; Deep learning; Machine learning; MRI; Prediction models; Stroke; Stroke outcome

Melanoma Segmentation and Classification Employing Imaging and Graph Encodings - Rivera Monroy, Luis Carlos; Eberhardt, Martin; Ostalecki, Christian; Baur, Andreas; Vera, Julio; Maier, Andreas (Proceedings - 2022 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2022, 2022)
Histopathology imaging is crucial for the diagnosis and the decision on treating associated tissue diseases. For this reason, Computer-assisted approaches have gained popularity and shown promise in tasks such as segmentation and classification. However, collecting a significant amount of data and high-quality annotations remains a challenge. This work describes a pipeline for toponomy profiling on the population of cells that combines melanoma multidimensional fluorescent imaging, a supervised segmentation of the region of interest, and graph unsupervised embedded representation.The Multi-Epitope-Ligand Cartography (MELC) provides a range of data shown to be optimal for solving supervised and unsuper-vised tasks. This imaging technique and the methodology, such as the one presented here, are promising for further improvement in the study of diagnostic techniques and therapies for skin diseases, such as melanoma.
CRIS-Team Scopus Importer:2023-03-07
Keywords: melanoma; microscopy; neural networks; segmentation

Multi-Channel Volumetric Neural Network for Knee Cartilage Segmentation in Cone-Beam CT - Maier, Jennifer; Rivera Monroy, Luis Carlos; Syben-Leisner, Christopher; Jeon, Yejin; Choi, Jang-Hwan; Hall, Mary; Levenston, Marc; Gold, Garry; Fahrig, Rebecca; Maier, Andreas (Bildverarbeitung für die Medizin 2020, 2020)
Analyzing knee cartilage thickness and strain under load can help to further the understanding of the effects of diseases like Osteoarthritis. A precise segmentation of the cartilage is a necessary prerequisite for this analysis. This segmentation task has mainly been addressed in Magnetic Resonance Imaging, and was rarely investigated on contrast-enhanced Computed Tomography, where contrast agent visualizes the border between femoral and tibial cartilage. To overcome the main drawback of manual segmentation, namely its high time investment, we propose to use a 3D Convolutional Neural Network for this task. The presented architecture consists of a V-Net with SeLu activation, and a Tversky loss function. Due to the high imbalance between very few cartilage pixels and many background pixels, a high false positive rate is to be expected. To reduce this rate, the two largest segmented point clouds are extracted using a connected component analysis, since they most likely represent the medial and lateral tibial cartilage surfaces. The resulting segmentations are compared to manual segmentations, and achieve on average a recall of 0.69, which confirms the feasibility of this approach.<br

Multi-level cancer profiling through joint cell-graph representations - Rivera Monroy, Luis Carlos; Rist, Leonhard; Wilm, Frauke; Ostalecki, Christian; Baur, Andreas; Vera González, Julio; Breininger, Katharina; Maier, Andreas (Smart Health, 2024)
Computer-aided analysis of digitized pathology samples has significantly advanced with the rapid progression of machine and Deep Learning (DL) methods. However, most existing approaches primarily focus on features extracted from patches due to the large image sizes. This focus limits the ability of Convolutional Neural Networks (CNNs) to capture global information from the samples, resulting in an incomplete phenotypical and topological representation and thereby restricting the diagnostic capabilities of these methods. The recent emergence of Graph Neural Networks (GNNs) offers new opportunities to overcome these limitations through graph-driven representations of pathological samples. This work introduces a graph-based framework that encompasses diverse cancer types and integrates different imaging modalities. In this framework, histopathology samples are represented as graphs, and a pipeline facilitating cell-wise and disease classification is developed. The results support this motivation: for cell-wise classification, we achieved an average accuracy of , and for disease-wise classification, an average accuracy of 83%, outperforming reference models such as XGBoost and standard CNNs. This approach not only provides flexibility in combining various diseases but also extends to integrating different staining technique
Keywords: Fluorescence microscopy imageGraph deep learningMultiplex imagingDigital pathology

Volumetric multimodality neural network for brain tumor segmentation - Silvana Castillo, Laura; Alexandra Daza, Laura; Rivera, Luis Carlos; Arbelaez, Pablo (Proceedings of SPIE - The International Society for Optical Engineering, 2017)
Brain lesion segmentation is one of the hardest tasks to be solved in computer vision with an emphasis on the medical field. We present a convolutional neural network that produces a semantic segmentation of brain tumors, capable of processing volumetric data along with information from multiple MRI modalities at the same time. This results in the ability to learn from small training datasets and highly imbalanced data. Our method is based on DeepMedic, the state of the art in brain lesion segmentation. We develop a new architecture with more convolutional layers, organized in three parallel pathways with different input resolution, and additional fully connected layers. We tested our method over the 2015 BraTS Challenge dataset, reaching an average dice coefficient of 84%, while the standard DeepMedic implementation reached 74%.
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Keywords: Brain tumor; Deep learning; MRI; Semantic segmentation