TY  - CONF
AB  - Reliable and stable performance is crucial for the application of computer-aided medical image systems in clinical settings. However, approaches based on deep learning often fail to generalize well under distribution shifts. In medical imaging, such distribution shifts can, for example, be introduced by changes in scanner types or imaging protocols. To counter this, test-time generalization aims to optimize a model that has been trained on single or multiple source domains to an unseen target domain. Common test-time adaptation methods fine-tune model weights utilizing losses with gradient-based optimization, a time-consuming and computationally demanding procedure. In contrast, our approach adopts a non-parametric method that is entirely feedforward and utilizes information from target samples to extract neighborhood information with dynamic voting. By doing so, we avoid fine-tuning or optimization procedures, enabling our method to be more efficient and achieve stable adaptation. We demonstrate the effectiveness of our approach by benchmarking it against different state-of-the-art methods with three backbones on two publicly available medical imaging datasets, consisting of fetal ultrasound and retinal images, and achieve classification accuracy improvements by up to 3.4% and 1.1%, respectively. Moreover, we also demonstrate the utility of our method in practical scenarios, proving efficiency in terms of computational runtime and handling of uncertainty. Our code is publicly available at: https://github.com/compai-lab/2024-miccai-emerge-ambekar.
AU  - Ambekar, S.
AU  - Schnabel, J.A.
AU  - Lang, D.M.
C1  - 73617
C2  - 57138
SP  - 224-234
TI  - Non-parametric neighborhood test-time generalization: Application to medical image classification.
JO  - Comm. Comp. Info. Sci.
VL  - 2240
PY  - 2025
SN  - 1865-0929
ER  - 

TY  - CONF
AB  - With the increasing incidence of neurodegenerative diseases such as Alzheimer’s Disease (AD), there is a need for further research that enhances detection and monitoring of the diseases. We present MORPHADE (Morphological Autoencoders for Alzheimer’s Disease Detection), a novel unsupervised learning approach which uses deformations to allow the analysis of 3D T1-weighted brain images. To the best of our knowledge, this is the first use of deformations with deep unsupervised learning to not only detect, but also localize and assess the severity of structural changes in the brain due to AD. We obtain markedly higher anomaly scores in clinically important areas of the brain in subjects with AD compared to healthy controls, showcasing that our method is able to effectively locate AD-related atrophy. We additionally observe a visual correlation between the severity of atrophy highlighted in our anomaly maps and medial temporal lobe atrophy scores evaluated by a clinical expert. Finally, our method achieves an AUROC of 0.80 in detecting AD, out-performing several supervised and unsupervised baselines. We believe our framework shows promise as a tool towards improved understanding, monitoring and detection of AD. To support further research and application, we have made our code publicly available at github.com/ci-ber/MORPHADE.
AU  - Avci, M.Y.*
AU  - Chan, E.
AU  - Zimmer, V.*
AU  - Rueckert, D.*
AU  - Wiestler, B.*
AU  - Schnabel, J.A.
AU  - Bercea, C.-I.
C1  - 73618
C2  - 57139
CY  - Gewerbestrasse 11, Cham, Ch-6330, Switzerland
SP  - 266-276
TI  - Unsupervised analysis of Alzheimer’s disease signatures using 3D deformable autoencoders.
JO  - Comm. Comp. Info. Sci.
VL  - 2240
PB  - Springer International Publishing Ag
PY  - 2025
SN  - 1865-0929
ER  - 

TY  - CONF
AB  - Identifying (a) systemic barriers to quality healthcare access and (b) key indicators of care efficacy in the United States remains a significant challenge. To improve our understanding of regional disparities in care delivery, we introduce a novel application of curvature, a geometrical-topological property of networks, to Physician Referral Networks. Our initial findings reveal that Forman-Ricci and Ollivier-Ricci curvature measures, which are known for their expressive power in characterizing network structure, offer promising indicators for detecting variations in healthcare efficacy while capturing a range of significant regional demographic features. We also present apparent, an open-source tool that leverages Ricci curvature and other network features to examine correlations between regional Physician Referral Networks structure, local census data, healthcare effectiveness, and patient outcomes.
AU  - Wayland, J.D.
AU  - Funk, R.J.*
AU  - Rieck, B.
C1  - 74688
C2  - 57559
SP  - 1-16
TI  - Characterizing physician referral networks with Ricci Curvature.
JO  - Comm. Comp. Info. Sci.
VL  - 2386 CCIS
PY  - 2025
SN  - 1865-0929
ER  -