1 citations · 1 across the 11 of their papers we have counts for
12 papers
A Principled Approach to Unsupervised Anomaly Detection
James Myles, Matthew Baugh, Johanna P. Müller +2
Traditional unsupervised anomaly detection (UAD) methods are designed to flag or localise deviations from a normative distribution, ignoring the underlying generative mechanisms of…
Panda: Unsupervised Pelvic Anomaly Detection for Real-Time MR Imaging
Anika Knupfer, Maximilian Lindholz, Johanna Paula Müller +4
Female pelvic diseases remain an under researched area characterized by often delayed diagnosis. While pelvic MRI offers superior soft-tissue contrast for diagnosis and image-guide…
Wasserstein Equilibrium Decoding for Reliable Medical Visual Question Answering
Luca Hagen, Johanna P. Müller, Weitong Zhang +2
Small vision-language models (2-8B) are well-suited for clinical deployment due to privacy constraints, limited connectivity, and low-latency requirements favouring on-device or on…
Geometry-Aware Uncertainty Coresets for Robust Visual In-Context Learning in Histopathology
Franciskus Xaverius Erick, Johanna Paula Müller, Bernhard Kainz
Vision-language models (VLMs) can couple visual perception with open-ended clinical reasoning, making them attractive for computational histopathology. However, fine-tuning billion…
Unsupervised Anomaly Detection of Diseases in the Female Pelvis for Real-Time MR Imaging
Anika Knupfer, Johanna P. Müller, Jordina A. Verdera +9
Pelvic diseases in women of reproductive age represent a major global health burden, with diagnosis frequently delayed due to high anatomical variability, complicating MRI interpre…
Label-free Motion-Conditioned Diffusion Model for Cardiac Ultrasound Synthesis
Zhe Li, Hadrien Reynaud, Johanna P Müller +1
Ultrasound echocardiography is essential for the non-invasive, real-time assessment of cardiac function, but the scarcity of labelled data, driven by privacy restrictions and the c…
Diffusing the Blind Spot: Uterine MRI Synthesis with Diffusion Models
Johanna P. Müller, Anika Knupfer, Pedro Blöss +3
Despite significant progress in generative modelling, existing diffusion models often struggle to produce anatomically precise female pelvic images, limiting their application in g…
L-FUSION: Laplacian Fetal Ultrasound Segmentation & Uncertainty Estimation
Johanna P. Müller, Robert Wright, Thomas G. Day +6
Accurate analysis of prenatal ultrasound (US) is essential for early detection of developmental anomalies. However, operator dependency and technical limitations (e.g. intrinsic ar…
Resource-efficient Medical Image Analysis with Self-adapting Forward-Forward Networks
Johanna P. Müller, Bernhard Kainz
We introduce a fast Self-adapting Forward-Forward Network (SaFF-Net) for medical imaging analysis, mitigating power consumption and resource limitations, which currently primarily…
Stochastic Vision Transformers with Wasserstein Distance-Aware Attention
Franciskus Xaverius Erick, Mina Rezaei, Johanna Paula Müller +1
Self-supervised learning is one of the most promising approaches to acquiring knowledge from limited labeled data. Despite the substantial advancements made in recent years, self-s…
Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis
Glejdis Shkëmbi, Johanna P. Müller, Zhe Li +3
Breast cancer is a major concern for women's health globally, with axillary lymph node (ALN) metastasis identification being critical for prognosis evaluation and treatment guidanc…
Adnexal Mass Segmentation with Ultrasound Data Synthesis
Clara Lebbos, Jen Barcroft, Jeremy Tan +5
Ovarian cancer is the most lethal gynaecological malignancy. The disease is most commonly asymptomatic at its early stages and its diagnosis relies on expert evaluation of transvag…