eess.IVMar 4, 2026

Self-Supervised Multi-Stage Domain Unlearning for White-Matter Lesion Segmentation

Authors: Domen Preložnik, Žiga Špiclin

Organizations: University of Ljubljana, Faculty of Electrical Engineering, Slovenia

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Abstract

Inter-scanner variability of magnetic resonance imaging has an adverse impact on the diagnostic and prognostic quality of the scans and necessitates the development of models robust to domain shift inflicted by the unseen scanner data. Review of recent advances in domain adaptation showed that efficacy of strategies involving modifications or constraints on the latent space appears to be contingent upon the level and/or depth of supervision during model training. In this paper, we therefore propose an unsupervised domain adaptation technique based on self-supervised multi-stage unlearning (SSMSU). Building upon the state-of-the-art segmentation framework nnU-Net, we employ deep supervision at deep encoder stages using domain classifier unlearning, applied sequentially across the deep stages to suppress domain-related latent features. Following self-configurable approach of the nnU-Net, the auxiliary feedback loop implements a self-supervised backpropagation schedule for the unlearning process, since continuous unlearning was found to have a detrimental effect on the main segmentation task. Experiments were carried out on four public datasets for benchmarking white-matter lesion segmentation methods. Five benchmark models and/or strategies, covering passive to active unsupervised domain adaptation, were tested. In comparison, the SSMSU demonstrated the advantage of unlearning by enhancing lesion sensitivity and limiting false detections, which resulted in higher overall segmentation quality in terms of segmentation overlap and relative lesion volume error. The proposed model inputs only the FLAIR modality, which simplifies preprocessing pipelines, eliminates the need for inter-modality registration errors and harmonization, which can introduce variability. Source code is available on https://github.com/Pubec/nnunetv2-unlearning.

Figures & tables

Figure 1. Comparison of MRI scans between different domains.
Source / SubsetVendorFSAMTrainTest
WMH / SeenPhilips3T2D2030
Siemens3T2D2030
WMH / UnseenPhilips3T3D 10
GE3T3D 30
GE1.5T3D 10
MSSEG / UnseenPhilips3T3D 15
Table 1. Dataset information and train / test splits as used in experimental evaluation.
Figure 2. (a) MRI preprocessing and (b) self-supervised multi-stage unlearning with nnU-Net.
Algorithm 1: Multi-Stage Self-Supervised Unlearning.
ee←\leftarrow Current epoch
ete_{t}←\leftarrow Total epochs
ewe_{w}←\leftarrow Warm-up epochs
ExE_{x}←\leftarrow Encoder block x={1,…,6}x=\{1,\ldots,6\}
CxC_{x}←\leftarrow Classifier x={1,…,6}x=\{1,\ldots,6\}
PatPat←\leftarrow Patience (on learning steps)
Table 4
Figure 3. Ablation study metric values with respect to learn-to-unlearn ratio (LUR=1-1, 2-1, 3-1, 4-1, 5-1), for various unlearning stages: A - from bottleneck, B - bottom three blocks, C - top three blocks, D - entire encoder .
Unlearning stages LURUnseen data - not used for model training: Philips 3T 3D, GE 3T 3D, GE 1.5T 3D
123456DSC ↑\uparrowTPR ↑\uparrowLTPR ↑\uparrowLFDR ↓\downarrowRVE ↓\downarrowRS ↓\downarrow
     ✓1 - 10.719 ± 0.1000.794 ± 0.1160.717 ± 0.0970.251 ± 0.1130.262 ± 0.2354.2
   ✓✓✓2 - 10.705 ± 0.1030.767 ± 0.1370.716 ± 0.1070.243 ± 0.1150.241 ± 0.2015.0
✓✓✓✓✓✓3 - 10.642 ± 0.1320.856 ± 0.0960.741 ± 0.1040.347 ± 0.1850.763 ± 0.5896.4
   ✓✓✓5 - 10.674 ± 0.1200.832 ± 0.1010.813 ± 0.0950.474 ± 0.1850.527 ± 0.4495.2
     ✓Self-superv.0.712 ± 0.0990.805 ± 0.1150.759 ± 0.1030.294 ± 0.1360.308 ± 0.2724.4
Table 2. Ablation study results on the 2017 WMH Segmentation Challenge’s unseen test split. Values are mean ±\pm standard deviation, while bold indicates best result for particular evaluation metric. LUR is the ratio of the number of learning vs. unlearning steps.
Model Classifier accuracy, AcciAcc^{i}
Unlearning stagesStepi=1i=2i=3i=4i=5i=6
nnU-Net* 100%100%100%100%100%100%
nnU-Net* SSMSUPost warm-up100%100%100%100%100%99%
Unlearning stages: 1 & 2 & 3 (Top 3)Post unlearning85%77%80%98%99%91%
nnU-Net* SSMSUPost warm-up100%99%99%99%99%99%
Unlearning stages: 4 & 5 & 6 (Bottom 3)Post unlearning100%100%100%67%48%50%
Table 3. Domain classifier accuracy, post warm-up and post-unlearning, per stage of the nnU-Net SSMSU on 2017 WMH dataset’s seen train split, comparing top- and bottom-three and unlearning of all stages. Values in bold indicate stages actively unlearned.
Figure 4. DSC with 95% confidence intervals for MSSEG 2016 unseen test set. For notes (*,**) see caption of Table 4 .
Figure 5. DSC with 95% confidence intervals per source domain of MSSEG unseen test set for nnU-Net SSMSU.
Figure 6. Axial cross-sections of MSSEG 2016 MR scans with superimposed manual ( top row ) and second best default nnU-Net ( middle row ) and the proposed SSMSU model segmentations ( bottom row ). Arrows highlight important differences.
WMH - seen test dataset
ModelDSC ↑\uparrowTPR ↑\uparrowLTPR ↑\uparrowLFDR ↓\downarrowRVE ↓\downarrowRS ↓\downarrow
nnU-Net No DA0.750 ± 0.120 †0.775 ± 0.132 †0.680 ± 0.160 †0.390 ± 0.160 †0.220 ± 0.3533.2
nnU-Net*0.773 ± 0.1080.785 ± 0.129 †0.679 ± 0.103 †0.210 ± 0.1310.205 ± 0.3882.0
nnU-Net* Preprocessing**0.732 ± 0.123 †0.748 ± 0.149 †0.630 ± 0.105 †0.219 ± 0.137 †0.232 ± 0.3713.8
Dinsdale et al. ( Dinsdale et al., 2021 )0.724 ± 0.149 †0.793 ± 0.123 †0.624 ± 0.108 †0.426 ± 0.187 †0.304 ± 0.4774.4
nnU-Net SSMSU (ours)0.772 ± 0.0970.810 ± 0.1240.718 ± 0.0970.262 ± 0.1600.197 ± 0.2281.6
Table 4. Segmentation evaluation on seen (WMH) and three unseen (MSSEG, MSLJ, ISBI) datasets, using respective seen and unseen test splits (Table 1 ). Values are mean ±\pm standard deviation, while bold indicates best results per each performance metric for methods in direct comparison.
Figure 7. Axial cross-sections of six MSSEG 2016 MR scans ( columns ) for the nnU-Net baseline ( top row ) and the proposed nnU-Net SSMSU ( bottom row ) with superimposed true positives ( green ), false positives ( blue ) and false negatives ( red ) ( bottom row ). Red frame indicates best result according to the DSC metric. Overall, nnU-Net baseline was best in 5, while the proposed nnU-Net SSMSU was best in 47 out of 51 test cases.
Figure 8. Lesion segmentation performance according to lesion size, i.e. small (SM), medium (MED) and large (LG); obtained on the MSSEG dataset.
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