Zobrazeno 1 - 10
of 118
pro vyhledávání: '"Mueller, Philip"'
In natural language processing and computer vision, self-supervised pre-training on large datasets unlocks foundational model capabilities across domains and tasks. However, this potential has not yet been realised in time series analysis, where exis
Externí odkaz:
http://arxiv.org/abs/2410.07299
Autor:
Dwedari, Mohammed Munzer, Consagra, William, Müller, Philip, Turgut, Özgün, Rueckert, Daniel, Rathi, Yogesh
The Orientation Distribution Function (ODF) characterizes key brain microstructural properties and plays an important role in understanding brain structural connectivity. Recent works introduced Implicit Neural Representation (INR) based approaches t
Externí odkaz:
http://arxiv.org/abs/2409.09387
Autor:
Holland, Robbie, Taylor, Thomas R. P., Holmes, Christopher, Riedl, Sophie, Mai, Julia, Patsiamanidi, Maria, Mitsopoulou, Dimitra, Hager, Paul, Müller, Philip, Scholl, Hendrik P. N., Bogunović, Hrvoje, Schmidt-Erfurth, Ursula, Rueckert, Daniel, Sivaprasad, Sobha, Lotery, Andrew J., Menten, Martin J.
Clinicians spend a significant amount of time reviewing medical images and transcribing their findings regarding patient diagnosis, referral and treatment in text form. Vision-language models (VLMs), which automatically interpret images and summarize
Externí odkaz:
http://arxiv.org/abs/2407.08410
Since the Transformer architecture emerged, language model development has grown, driven by their promising potential. Releasing these models into production requires properly understanding their behavior, particularly in sensitive domains like medic
Externí odkaz:
http://arxiv.org/abs/2406.16611
Field-of-view (FOV) recovery of truncated chest CT scans is crucial for accurate body composition analysis, which involves quantifying skeletal muscle and subcutaneous adipose tissue (SAT) on CT slices. This, in turn, enables disease prognostication.
Externí odkaz:
http://arxiv.org/abs/2406.04769
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 process through user queries) and localized interpretability (i.e. vi
Externí odkaz:
http://arxiv.org/abs/2404.15770
Autor:
Li, Jun, Kim, Su Hwan, Müller, Philip, Felsner, Lina, Rueckert, Daniel, Wiestler, Benedikt, Schnabel, Julia A., Bercea, Cosmin I.
This research explores the integration of language models and unsupervised anomaly detection in medical imaging, addressing two key questions: (1) Can language models enhance the interpretability of anomaly detection maps? and (2) Can anomaly maps im
Externí odkaz:
http://arxiv.org/abs/2404.07622
Weakly supervised object detection (WSup-OD) increases the usefulness and interpretability of image classification algorithms without requiring additional supervision. The successes of multiple instance learning in this task for natural images, howev
Externí odkaz:
http://arxiv.org/abs/2402.11985
Pathology detection and delineation enables the automatic interpretation of medical scans such as chest X-rays while providing a high level of explainability to support radiologists in making informed decisions. However, annotating pathology bounding
Externí odkaz:
http://arxiv.org/abs/2309.02578
Autor:
Turgut, Özgün, Müller, Philip, Hager, Paul, Shit, Suprosanna, Starck, Sophie, Menten, Martin J., Martens, Eimo, Rueckert, Daniel
The electrocardiogram (ECG) is a widely available diagnostic tool that allows for a cost-effective and fast assessment of the cardiovascular health. However, more detailed examination with expensive cardiac magnetic resonance (CMR) imaging is often p
Externí odkaz:
http://arxiv.org/abs/2308.05764