4 citations · 11 across the 20 of their papers we have counts for
23 papers
Reading the Whole Heart: Latent-Attention Masked Autoencoders for Multimodal Cardiac Representation Learning
Andrea Agostini, Simon Böhi, Moritz Vandenhirtz +7
Cardiovascular diagnosis and treatment rest on integrating complementary modalities, such as electrocardiogram, echocardiography, and chest X-rays, each capturing distinct but comp…
Bowel Obstruction Detection and Localization on Abdominal CT with Deep Learning
Moritz Vandenhirtz, Andrea Agostini, Dana Belde +5
Bowel obstruction is a common and potentially life-threatening gastrointestinal condition. In the face of rising diagnostic workloads, the automated diagnosis of bowel obstruction…
Foundation Model for Cardiac Time Series via Masked Latent Attention
Moritz Vandenhirtz, Samuel Ruipérez-Campillo, Simon Böhi +6
Electrocardiograms (ECGs) are among the most widely available clinical signals and play a central role in cardiovascular diagnosis. While recent foundation models (FMs) have shown…
Beyond Independent Frames: Latent Attention Masked Autoencoders for Multi-View Echocardiography
Simon Böhi, Irene Cannistraci, Sergio Muñoz Gonzalez +8
Echocardiography is a widely used modality for cardiac assessment due to its non-invasive and cost-effective nature, but the sparse and heterogeneous spatiotemporal views of the he…
Models Designed to Forget: Machine Unlearning via Key Deletion
Sonia Laguna, Jorge da Silva Goncalves, Moritz Vandenhirtz +3
Machine unlearning for vision models is rapidly becoming a practical requirement, driven by privacy regulations, data errors, and the need to remove harmful or corrupted training i…
Multimodal Generative Recommendation for Fusing Semantic and Collaborative Signals
Moritz Vandenhirtz, Kaveh Hassani, Shervin Ghasemlou +5
Sequential recommender systems rank relevant items by modeling a user's interaction history and computing the inner product between the resulting user representation and stored ite…
Structure is Supervision: Multiview Masked Autoencoders for Radiology
Sonia Laguna, Andrea Agostini, Alain Ryser +9
Building robust medical machine learning systems requires pretraining strategies that exploit the intrinsic structure present in clinical data. We introduce Multiview Masked Autoen…
Post-hoc Stochastic Concept Bottleneck Models
Wiktor Jan Hoffmann, Sonia Laguna, Moritz Vandenhirtz +2
Concept Bottleneck Models (CBMs) are interpretable models that predict the target variable through high-level human-understandable concepts, allowing users to intervene on mispredi…
Leveraging the Structure of Medical Data for Improved Representation Learning
Andrea Agostini, Sonia Laguna, Alain Ryser +7
Building generalizable medical AI systems requires pretraining strategies that are data-efficient and domain-aware. Unlike internet-scale corpora, clinical datasets such as MIMIC-C…
Interpretable Diffusion Models with B-cos Networks
Nicola Bernold, Moritz Vandenhirtz, Alice Bizeul +1
Text-to-image diffusion models generate images by iteratively denoising random noise, conditioned on a prompt. While these models have enabled impressive progress in image generati…
From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection
Moritz Vandenhirtz, Julia E. Vogt
Understanding the decision-making process of machine learning models provides valuable insights into the task, the data, and the reasons behind a model's failures. In this work, we…
Measuring Leakage in Concept-Based Methods: An Information Theoretic Approach
Mikael Makonnen, Moritz Vandenhirtz, Sonia Laguna +1
Concept Bottleneck Models (CBMs) aim to enhance interpretability by structuring predictions around human-understandable concepts. However, unintended information leakage, where pre…
RadVLM: A Multitask Conversational Vision-Language Model for Radiology
Nicolas Deperrois, Hidetoshi Matsuo, Samuel Ruipérez-Campillo +12
The widespread use of chest X-rays (CXRs), coupled with a shortage of radiologists, has driven growing interest in automated CXR analysis and AI-assisted reporting. While existing…
Exploiting Interpretable Capabilities with Concept-Enhanced Diffusion and Prototype Networks
Alba Carballo-Castro, Sonia Laguna, Moritz Vandenhirtz +1
Concept-based machine learning methods have increasingly gained importance due to the growing interest in making neural networks interpretable. However, concept annotations are gen…
TreeDiffusion: Hierarchical Generative Clustering for Conditional Diffusion
Jorge da Silva Gonçalves, Laura Manduchi, Moritz Vandenhirtz +1
Generative modeling and clustering are conventionally distinct tasks in machine learning. Variational Autoencoders (VAEs) have been widely explored for their ability to integrate b…
From Logits to Hierarchies: Hierarchical Clustering made Simple
Emanuele Palumbo, Moritz Vandenhirtz, Alain Ryser +2
The hierarchical structure inherent in many real-world datasets makes the modeling of such hierarchies a crucial objective in both unsupervised and supervised machine learning. Whi…
Structured Generations: Using Hierarchical Clusters to guide Diffusion Models
Jorge da Silva Goncalves, Laura Manduchi, Moritz Vandenhirtz +1
This paper introduces Diffuse-TreeVAE, a deep generative model that integrates hierarchical clustering into the framework of Denoising Diffusion Probabilistic Models (DDPMs). The p…
scTree: Discovering Cellular Hierarchies in the Presence of Batch Effects in scRNA-seq Data
Moritz Vandenhirtz, Florian Barkmann, Laura Manduchi +2
We propose a novel method, scTree, for single-cell Tree Variational Autoencoders, extending a hierarchical clustering approach to single-cell RNA sequencing data. scTree corrects f…
Stochastic Concept Bottleneck Models
Moritz Vandenhirtz, Sonia Laguna, Ričards Marcinkevičs +1
Concept Bottleneck Models (CBMs) have emerged as a promising interpretable method whose final prediction is based on intermediate, human-understandable concepts rather than the raw…
Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable?
Sonia Laguna, Ričards Marcinkevičs, Moritz Vandenhirtz +1
Recently, interpretable machine learning has re-explored concept bottleneck models (CBM). An advantage of this model class is the user's ability to intervene on predicted concept v…
This Reads Like That: Deep Learning for Interpretable Natural Language Processing
Claudio Fanconi, Moritz Vandenhirtz, Severin Husmann +1
Prototype learning, a popular machine learning method designed for inherently interpretable decisions, leverages similarities to learned prototypes for classifying new data. While…
Tree Variational Autoencoders
Laura Manduchi, Moritz Vandenhirtz, Alain Ryser +1
We propose Tree Variational Autoencoder (TreeVAE), a new generative hierarchical clustering model that learns a flexible tree-based posterior distribution over latent variables. Tr…
Signal Is Harder To Learn Than Bias: Debiasing with Focal Loss
Moritz Vandenhirtz, Laura Manduchi, Ričards Marcinkevičs +1
Spurious correlations are everywhere. While humans often do not perceive them, neural networks are notorious for learning unwanted associations, also known as biases, instead of th…