From the 1 of 27 linked papers with an AI index.
2 citations · 2 across the 7 of their papers we have counts for
6 papers · 1 filter
Big, Bright, or Invisible: A Frozen-Feature Benchmark of 3D CT Foundation Models
Maulik Chevli, Johannes Brandt, Rickmer Braren +2
Routine CT interpretation is inherently comprehensive, capturing incidental findings across the entire scan volume. 3D CT foundation models could assist this process by providing g…
LungEvaty: A Scalable, Open-Source Transformer-based Deep Learning Model for Lung Cancer Risk Prediction in LDCT Screening
Johannes Brandt, Maulik Chevli, Rickmer Braren +3
Lung cancer risk estimation is gaining increasing importance as more countries introduce population-wide screening programs using low-dose CT (LDCT). As imaging volumes grow, scala…
Whole-body Representation Learning For Competing Preclinical Disease Risk Assessment
Dmitrii Seletkov, Sophie Starck, Ayhan Can Erdur +3
Reliable preclinical disease risk assessment is essential to move public healthcare from reactive treatment to proactive identification and prevention. However, image-based risk pr…
Cross-domain and Cross-dimension Learning for Image-to-Graph Transformers
Alexander H. Berger, Laurin Lux, Suprosanna Shit +5
Direct image-to-graph transformation is a challenging task that involves solving object detection and relationship prediction in a single model. Due to this task's complexity, larg…
How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised Anomaly Detection
Felix Meissen, Johannes Getzner, Alexander Ziller +4
Unsupervised anomaly detection (UAD) alleviates large labeling efforts by training exclusively on unlabeled in-distribution data and detecting outliers as anomalies. Generally, the…
ChEX: Interactive Localization and Region Description in Chest X-rays
Philip Müller, Georgios Kaissis, Daniel Rueckert
Report generation models offer fine-grained textual interpretations of medical images like chest X-rays, yet they often lack interactivity (i.e. the ability to steer the generation…