A Lightweight Semi-Supervised Brain Tumor Segmentation Network with Counterfactual Reasoning

FAN Yawen; WANG Chaoyuan; WANG Xin; ZHANG Xinchen; ZHOU Quan

doi:10.11999/JEIT251130

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FAN Yawen, WANG Chaoyuan, WANG Xin, ZHANG Xinchen, ZHOU Quan. A Lightweight Semi-Supervised Brain Tumor Segmentation Network with Counterfactual Reasoning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251130

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FAN Yawen, WANG Chaoyuan, WANG Xin, ZHANG Xinchen, ZHOU Quan. A Lightweight Semi-Supervised Brain Tumor Segmentation Network with Counterfactual Reasoning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251130

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A Lightweight Semi-Supervised Brain Tumor Segmentation Network with Counterfactual Reasoning

doi: 10.11999/JEIT251130 cstr: 32379.14.JEIT251130

National Engineering Research Center of Communications and Networking, Nanjing University of Posts Telecommunications, Nanjing 210003, China

Funds: The National Natural Science Foundation of China(62476139), Postgraduate Research & Practice Innovation Program of Jiangsu Province (SJCX25_0308)

Accepted Date: 2026-04-15
Rev Recd Date: 2026-04-15

Available Online: 2026-04-30

Abstract

Abstract

Objective Brain tumor segmentation plays an essential role in clinical diagnosis and treatment planning. However, creating reliable labels for medical images is costly and time-consuming, making it difficult to obtain large annotated datasets. To address this challenge, we propose a semi-supervised brain tumor segmentation approach that combines a lightweight backbone network with a counterfactual reasoning strategy. The study aims to improve segmentation accuracy while keeping the model efficient enough for real clinical use. Methods We design a multi-modal encoder–decoder network with shared parameters to reduce the model size and computation. The network incorporates anatomical structure consistency as prior knowledge, helping it better align with the underlying brain anatomy. During training, a teacher–student framework is used to generate counterfactual samples from their predictions. These samples guide the learning of unlabeled data through a counterfactual loss function that enforces pixel-level consistency and feature-level stability. This strategy helps the model capture useful structural information from unlabeled scans without relying on artificial data augmentations that may distort tumor boundaries. Results and Discussions Experiments conducted on the BraTS2019 and BraTS2021 datasets demonstrate that the proposed method consistently outperforms other models under limited-label conditions. On BraTS2019, our approach achieves the best performance in terms of DSC (66.06%), and its IoU (53.16%) is comparable to other models. More importantly, it attains the lowest HD95 of 7.60 mm, representing an 11% and 6% reduction compared to UNet3D and LightMU-Net, respectively (Table 2 and 3). On BraTS2019, the proposed method improves DSC and IoU by 4–7% on average, while reducing HD95 by 0.6 mm (Table 4 and 5). The model is also highly efficient, with only 1.657M parameters, 0.4402T FLOPs, and an inference time of 0.0937 s per frame (Table 6). These results confirm that the optimized design effectively balances segmentation accuracy, computational efficiency, and clinical usability. The improvements come from both the lightweight network design and the counterfactual mechanism, which encourages the model to learn anatomically meaningful representations. Conclusions The proposed framework provides a simple yet effective solution for semi-supervised brain tumor segmentation. It offers a good balance between accuracy, efficiency, and interpretability, and demonstrates how causal reasoning can be practically integrated into medical image analysis.
- Brain tumor segmentation,
- Multimodal fusion,
- Lightweight,
- Semi-supervised learning,
- Counterfactual Reasoning

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References(24)

References

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