arXiv subjects

Alexander Geiger

Publications and source records attributed to Alexander Geiger.

6 recordsLinked to original sources

arXiv2026

Multimodal Graph-based Classification of Esophageal Motility Disorders

Diagnosing esophageal motility disorders pose significant challenges due to the complexity of high-resolution impedance manometry (HRIM) data and variability in clinical interpretation. This work explores the feasibility of a multimodal Machine Learning (ML)-based classification approach that combines HRIM recordings with patient-specific information and incorporates a graph-based modeling of esophageal physiology. We analyze HRIM recordings with corresponding patient information from 104 patients with esophageal motility disorders. Patient data includes demographic, clinical, and symptom information extracted from structured questionnaires and free-text notes using keyword detection and large language model-based processing. HRIM data is represented as spatio-temporal graphs, where nodes correspond to pressure values along the esophagus and edges encode spatial adjacency and impedance dynamics. A graph neural network (GNN) is applied to learn physiologically meaningful representations, which are fused with patient embeddings for multi-category, multi-class classification of swallow events. The impact of patient features and graph-based modeling is evaluated by ablation studies and comparison to vision-based classifier baselines. The proposed multimodal approach indicates improvements over models that rely solely on HRIM-derived features across all classification categories. Additionally, the graph-based modeling provides gains compared to vision-based baselines. Our experiments systematically assess the complementary contribution of multiple modalities, as well as demonstrate the feasibility of our proposed graph-based approach. Our initial findings demonstrate that integrating patient-level data with graph-based representations of HRIM signals appears to be a promising direction for more accurate classification of esophageal motility disorders.

cs.LG↗

arXiv2025

On the notion of missingness for path attribution explainability methods in medical settings: Guiding the selection of medically meaningful baselines

The explainability of deep learning models remains a significant challenge, particularly in the medical domain where interpretable outputs are essential for clinical trust and transparency. Path attribution methods such as Integrated Gradients rely on a baseline that represents the absence of informative features, a notion commonly referred to as missingness. Standard baselines, such as all-zero inputs, are often semantically meaningless in medical contexts, where intensity values carry clinical significance. In this work, we revisit the notion of missingness for medical imaging, expose the limitations of standard baselines in this setting, and formalize a stricter missingness we term semantic missingness: a baseline must not merely lack signal, but must represent a clinically plausible state in which the disease-related features are absent. This formulation motivates a counterfactual-guided approach to baseline selection, in which a synthetically generated counterfactual (i.e. a clinically normal variant of the pathological input) serves as a principled and semantically meaningful reference. We derive theoretical guarantees showing that counterfactual baselines yield more faithful attributions than standard alternatives, and empirically validate this with two complementary counterfactual generative models, a VAE and a diffusion model, though the concept is model-agnostic and compatible with any suitable counterfactual method. Across three diverse medical datasets, counterfactual baselines produce more faithful and medically relevant attributions, outperforming standard baseline choices as well as related methods. Notably, we also compare against using the counterfactual directly as an explanation (an established paradigm in its own) and show that employing it as a baseline for Integrated Gradients yields superior results, thereby bridging two complementary explainability paradigms.

cs.LG↗

arXiv2025

Towards Safety and Security Testing of Cyberphysical Power Systems by Shape Validation

The increasing complexity of cyberphysical power systems leads to larger attack surfaces to be exploited by malicious actors and a higher risk of faults through misconfiguration. We propose to meet those risks with a declarative approach to describe cyberphysical power systems and to automatically evaluate security and safety controls. We leverage Semantic Web technologies as a well-standardized framework, providing languages to specify ontologies, rules and shape constraints. We model infrastructure through an ontology which combines external ontologies, architecture and data models for sufficient expressivity and interoperability with external systems. The ontology can enrich itself through rules defined in SPARQL, allowing for the inference of knowledge that is not explicitly stated. Through the evaluation of SHACL shape constraints we can then validate the data and verify safety and security constraints. We demonstrate this concept with two use cases and illustrate how this solution can be developed further in a community-driven fashion.

cs.CR↗

arXiv2024

Detecting and clustering swallow events in esophageal long-term high-resolution manometry

High-resolution manometry (HRM) is the gold standard in diagnosing esophageal motility disorders. As HRM is typically conducted under short-term laboratory settings, intermittently occurring disorders are likely to be missed. Therefore, long-term (up to 24h) HRM (LTHRM) is used to gain detailed insights into the swallowing behavior. However, analyzing the extensive data from LTHRM is challenging and time consuming as medical experts have to analyze the data manually, which is slow and prone to errors. To address this challenge, we propose a Deep Learning based swallowing detection method to accurately identify swallowing events and secondary non-deglutitive-induced esophageal motility disorders in LTHRM data. We then proceed with clustering the identified swallows into distinct classes, which are analyzed by highly experienced clinicians to validate the different swallowing patterns. We evaluate our computational pipeline on a total of 25 LTHRMs, which were meticulously annotated by medical experts. By detecting more than 94% of all relevant swallow events and providing all relevant clusters for a more reliable diagnostic process among experienced clinicians, we are able to demonstrate the effectiveness as well as positive clinical impact of our approach to make LTHRM feasible in clinical care.

cs.CV↗

arXiv2021

AQEyes: Visual Analytics for Anomaly Detection and Examination of Air Quality Data

Anomaly detection plays a key role in air quality analysis by enhancing situational awareness and alerting users to potential hazards. However, existing anomaly detection approaches for air quality analysis have their own limitations regarding parameter selection (e.g., need for extensive domain knowledge), computational expense, general applicability (e.g., require labeled data), interpretability, and the efficiency of analysis. Furthermore, the poor quality of collected air quality data (inconsistently formatted and sometimes missing) also increases the difficulty of analysis substantially. In this paper, we systematically formulate design requirements for a system that can solve these limitations and then propose AQEyes, an integrated visual analytics system for efficiently monitoring, detecting, and examining anomalies in air quality data. In particular, we propose a unified end-to-end tunable machine learning pipeline that includes several data pre-processors and featurizers to deal with data quality issues. The pipeline integrates an efficient unsupervised anomaly detection method that works without the use of labeled data and overcomes the limitations of existing approaches. Further, we develop an interactive visualization system to visualize the outputs from the pipeline. The system incorporates a set of novel visualization and interaction designs, allowing analysts to visually examine air quality dynamics and anomalous events in multiple scales and from multiple facets. We demonstrate the performance of this pipeline through a quantitative evaluation and show the effectiveness of the visualization system using qualitative case studies on real-world datasets.

cs.HC↗

arXiv2020

TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks

Time series anomalies can offer information relevant to critical situations facing various fields, from finance and aerospace to the IT, security, and medical domains. However, detecting anomalies in time series data is particularly challenging due to the vague definition of anomalies and said data's frequent lack of labels and highly complex temporal correlations. Current state-of-the-art unsupervised machine learning methods for anomaly detection suffer from scalability and portability issues, and may have high false positive rates. In this paper, we propose TadGAN, an unsupervised anomaly detection approach built on Generative Adversarial Networks (GANs). To capture the temporal correlations of time series distributions, we use LSTM Recurrent Neural Networks as base models for Generators and Critics. TadGAN is trained with cycle consistency loss to allow for effective time-series data reconstruction. We further propose several novel methods to compute reconstruction errors, as well as different approaches to combine reconstruction errors and Critic outputs to compute anomaly scores. To demonstrate the performance and generalizability of our approach, we test several anomaly scoring techniques and report the best-suited one. We compare our approach to 8 baseline anomaly detection methods on 11 datasets from multiple reputable sources such as NASA, Yahoo, Numenta, Amazon, and Twitter. The results show that our approach can effectively detect anomalies and outperform baseline methods in most cases (6 out of 11). Notably, our method has the highest averaged F1 score across all the datasets. Our code is open source and is available as a benchmarking tool.

cs.LG↗