Abstract
Deep learning models in medical imaging often encounter
challenges when adapting to new clinical settings unseen during training.
Test-time adaptation offers a promising approach to optimize models for
these unseen domains, yet its application in anomaly detection (AD) re-
mains largely unexplored. AD aims to efficiently identify deviations from
normative distributions; however, full adaptation, including pathological
shifts, may inadvertently learn the anomalies it intends to detect. We in-
troduce a novel concept of selective test-time adaptation that utilizes the
inherent characteristics of deep pre-trained features to adapt selectively
in a zero-shot manner to any test image from an unseen domain. This
approach employs a model-agnostic, lightweight multi-layer perceptron for
neural implicit representations, enabling the adaptation of outputs from
any reconstruction-based AD method without altering the source-trained
model. Rigorous validation in brain AD demonstrated that our strategy
substantially enhances detection accuracy for multiple conditions and dif-
ferent target distributions. Specifically, our method improves the detection
rates by up to 78% for enlarged ventricles and 24% for edemas. Our code
is available: https://github.com/compai-lab/2024-miccai-adsmi-ambekar.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/ADSMI2024_107.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: https://papers.miccai.org/miccai-2026-sat/supp/ADSMI2024_107_supp.pdf
Link to Open Review
Open Review Page: Not Available
BibTex
@InProceedings{AmbSam_Selective_MICCAISAT2026,
author = { Ambekar, Sameer AND Schnabel, Julia A. AND Bercea, Cosmin I.},
title = { { Selective Test-Time Adaptation for Unsupervised Anomaly Detection using Neural Implicit Representations } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17276},
month = {pending},
page = {pending}
}