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Abstract
Pontine infarction is a high-risk subtype of ischemic stroke, which can easily lead to severe neurological dysfunction and even death, posing a high risk of disability and death. Accurate infarct segmentation and early prediction of Early Neurological Deterioration (END) are critical for treatment planning. However, prior work often treats these tasks separately or lacks explicit inter-task information transfer in multi-task settings. This results in the inability of segmentation-learned evidence of lesion structure to be used in classification tasks, while the risk discrimination signals of classification tasks cannot inversely constrain segmentation, limiting its reliable use in clinical decision-making. We propose PINet, a multimodal multi-task framework that jointly performs infarct lesion segmentation and END prediction with explicit cross-task feature interaction. PINet integrates Discrete Wavelet Transform (DWT) and its inverse into an encoder–decoder to replace pooling/upsampling, preserving high-frequency boundary details and improving small-lesion representation. A dual-task guided fusion module is designed to deeply fuse MRI features with clinical variables and learn task-specific representations. To model slice-wise continuity and long-range non-local dependencies in brain MRI, we further introduce a Mamba-based state-space global aggregation module that accumulates global context via sequential state propagation. Finally, a multimodal interaction classifier captures imaging–clinical correlations to enhance END prediction. Experiments on an in-house dataset of 386 patients with five-fold cross-validation show PINet consistently outperforms state-of-the-art methods on both tasks, demonstrating the benefit of explicit inter-task interaction and multimodal global-context modeling for stroke risk assessment.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026/paper/1824_paper.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to the Code Repository
https://github.com/LDS151/PINet
Link to the Dataset(s)
N/A
BibTex
@InProceedings{LiuJin_PINet_MICCAI2026,
author = { Liu, Jinshuo AND Ge, Ruiquan AND Zhang, Ning AND Wu, Beining AND Yao, Xinyan AND Wang, Zhiyao AND Wang, Changmiao AND Jia, Gangyong AND Wan, Xiang AND Huang, Ruyue AND Pan, Yi},
title = { { PINet: A Multimodal Network for Pontine Infarction Segmentation and Early Neurological Deterioration Prediction } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 16884},
month = {September},
page = {pending}
}
Reviews
Review #1
- Please describe the contribution of the paper
The main contribution of this paper is the proposal of PINet, a novel multimodal and multi-task deep learning framework designed for the simultaneous segmentation of pontine infarction (PI) lesions and the prediction of Early Neurological Deterioration (END). Unlike prior works that treat diagnosis and prognosis as isolated tasks, this paper successfully demonstrates that jointly learning lesion segmentation and END prediction allows the two tasks to mutually benefit from each other.
Specifically, the key contributions include:
Multimodal & Multi-task Integration: Effectively fusing 3D MRI imaging data with tabular clinical data to perform dual tasks.
Architectural Innovations: Introducing three novel components to handle the unique challenges of PI:
Discrete Wavelet Transform (DWT) to preserve fine-grained boundaries of small lesions instead of traditional pooling.
Dual-Task Guided Fusion Module (DGFM) to bridge the semantic gap between visual and clinical features.
Mamba Global Feature Aggregator (MGFA) to capture long-range spatial dependencies across whole-brain MRI slices.
Clinical Validation: Achieving state-of-the-art performance (Dice score 90.9% for segmentation and AUC 90.9% for END prediction) on a clinical dataset of 386 patients.
- Please list the major strengths of the paper: you should highlight a novel formulation, an original way to use data, demonstration of clinical feasibility, a novel application, a particularly strong evaluation, or anything else that is a strong aspect of this work. Please provide details, for instance, if a method is novel, explain what aspect is novel and why this is interesting.
1.Novel Formulation (Multi-task Learning): The integration of lesion segmentation and END prediction into a single unified multi-task network is highly innovative. The formulation cleverly allows morphological features from segmentation to inform the prognosis, while prognostic features help refine lesion boundaries, demonstrating a strong complementary effect.
2.Original Way to Use Data (Multimodal Integration): The paper effectively utilizes both 3D MRI scans and tabular clinical data (e. g. , patient demographics, vital signs). The proposed Dual-Task Guided Fusion Module (DGFM) provides an elegant solution to bridge the semantic gap between highly heterogeneous data types.
3.Strong Architectural Innovation: The technical design of the network is highly tailored to the clinical problem. Using Discrete Wavelet Transform (DWT) instead of standard pooling successfully preserves the fine-grained details and textures of small pontine infarctions. Furthermore, integrating the Mamba architecture (MGFA) to model long-range dependencies across MRI slices is a very modern and robust approach.
4.Demonstration of Clinical Feasibility & Strong Evaluation: The model was validated on a solid clinical dataset of 386 patients, achieving impressive state-of-the-art results (Dice score of 90.9% and AUC of 90.9%). This extensive evaluation clearly demonstrates its strong clinical feasibility and reliability for real-world stroke management.
- Please list the major weaknesses of the paper. Please provide details: for instance, if you state that a formulation, way of using data, demonstration of clinical feasibility, or application is not novel, then you must provide specific references to prior work.
1.Lack of Multi-center External Validation: While the dataset of 386 patients is substantial, it appears to be sourced from a single institution. This raises concerns about the model’s generalizability. Different hospitals use varying MRI scanner protocols, field strengths (e. g. , 1.5T vs 3T), and treat diverse demographic populations. The paper would benefit from evaluating the model on an external, multi-center cohort to prove its robustness.
2.Computational Complexity and Efficiency: The integration of 3D CNNs, multimodal data fusion, multi-task learning, and the Mamba global feature aggregator likely results in a computationally heavy model. The paper lacks a detailed discussion on the model’s computational footprint (e. g. , inference time, memory consumption, FLOPs/Params), which is a critical factor for deployment in resource-constrained clinical settings.
3.Limited Clinical Interpretability: Although the model achieves high accuracy in END prediction, it lacks transparency. In a clinical environment, it is crucial for physicians to understand why the model made a certain prediction. The paper does not provide sufficient Explainable AI (XAI) analyses (e. g. , feature importance scores for the clinical tabular data or activation maps) to show which specific clinical or visual features drove the END prediction.
- Please rate the clarity and organization of this paper
Good
- Please comment on the reproducibility of the paper. Please be aware that providing code and data is a plus, but not a requirement for acceptance.
The submission has provided an anonymized link to the source code, dataset, or any other dependencies.
- Based on your review and your understanding of the MICCAI Scientific Code of Ethics, do you believe this submission may involve a potential ethics concern or violation?
N/A
- Optional: If you have any additional comments to share with the authors, please provide them here. Please also refer to our Reviewer’s guide on what makes a good review and pay specific attention to the different assessment criteria for the different paper categories: https://conferences.miccai.org/2026/en/REVIEWER-GUIDELINES.html
N/A
- Rate the paper on a scale of 1-6, 6 being the strongest (6-4: accept; 3-1: reject). Please use the entire range of the distribution. Spreading the score helps create a distribution for decision-making.
(5) Accept — should be accepted, independent of rebuttal
- Please justify your recommendation. What were the major factors that led you to your overall score for this paper?
I lean towards a positive recommendation for this paper. The major factors that led to my overall score are as follows:
1.High Clinical Relevance and Novel Formulation: The paper tackles a highly critical and challenging clinical problem—pontine infarctions (PI). The transition from treating lesion segmentation and Early Neurological Deterioration (END) prediction as isolated tasks to a unified, mutually beneficial multi-task framework is both novel and highly practical for real-world stroke management.
2.Solid Technical Execution: The authors have proposed a very well-thought-out architecture tailored specifically to the challenges of PI. Replacing traditional pooling with Discrete Wavelet Transform (DWT) to preserve small lesion boundaries is a clever choice. Furthermore, the Dual-Task Guided Fusion Module (DGFM) successfully addresses the difficult task of bridging the semantic gap between 3D MRI visual features and tabular clinical data. The inclusion of the Mamba architecture (MGFA) for global context modeling further strengthens the technical soundness of the paper.
3.Strong Empirical Results: The extensive experiments conducted on a reasonable-sized clinical dataset (386 patients) convincingly demonstrate that PINet achieves state-of-the-art performance in both segmentation (Dice 90.9) and END prediction (AUC 90.9).
Balancing Factors: While I noted some weaknesses—specifically the lack of multi-center external validation and limited discussions on clinical interpretability (Explainable AI)—I believe these are common limitations in this specific domain and can be addressed in future work. They do not significantly diminish the value of the core contributions. The strong architectural innovations and impressive multi-task performance ultimately outweigh these limitations, justifying a favorable evaluation.
- Reviewer confidence
Confident but not absolutely certain (3)
- [Post rebuttal] After reading the authors’ rebuttal, please state your final opinion of the paper.
N/A
- [Post rebuttal] Please justify your final decision from above.
N/A
Review #2
- Please describe the contribution of the paper
The main contribution of this paper is the development of a multimodal framework in which classification of Early Neurological Deterioration (END) is combined with a wavelet-based segmentation network.
- Please list the major strengths of the paper: you should highlight a novel formulation, an original way to use data, demonstration of clinical feasibility, a novel application, a particularly strong evaluation, or anything else that is a strong aspect of this work. Please provide details, for instance, if a method is novel, explain what aspect is novel and why this is interesting.
The main strength of the paper is the multimodal integration framework, specifically the Multimodal Second-Order Fusion Classifier (MSOFC). This is a clever way to fuse imaging with scalar data and could have broad implications.
- Please list the major weaknesses of the paper. Please provide details: for instance, if you state that a formulation, way of using data, demonstration of clinical feasibility, or application is not novel, then you must provide specific references to prior work.
There are several weaknesses with this paper. First, the justification given for the method is not adequate, especially given the narrow application. It would have been much more convincing to see the framework applied across different types of stroke. There is a lot of complexity added to this model in comparison to something like an nnU-Net combined with a traditional classifier, and it seems the results do not justify this added complexity since there were only minimal gains. Second, Spearman correlations and other clinical variables were mentioned in the methods but were never seen in the results. Third, it is not clear how the data were split. With 386 cases, there should be ample data for a held-out test set to evaluate the model instead of 5-fold CV. Fourth, there is no limitations section discussion the pitfalls of this approach nor are there figures showing where the network failed. Lastly, and perhaps most importantly, I am not sure this method is clinically feasible or applicable. While pontine stroke is a devastating disease, they make up about 10% of total ischemic stroke cases, and clinicians are typically aware that the patient will not do well. The project would have been greatly strengthened by the inclusion of non-pontine ischemic strokes. There are other weaknesses throughout, but these are the ones that stood out the most.
- Please rate the clarity and organization of this paper
Satisfactory
- Please comment on the reproducibility of the paper. Please be aware that providing code and data is a plus, but not a requirement for acceptance.
The submission has provided an anonymized link to the source code, dataset, or any other dependencies.
- Based on your review and your understanding of the MICCAI Scientific Code of Ethics, do you believe this submission may involve a potential ethics concern or violation?
N/A
- Optional: If you have any additional comments to share with the authors, please provide them here. Please also refer to our Reviewer’s guide on what makes a good review and pay specific attention to the different assessment criteria for the different paper categories: https://conferences.miccai.org/2026/en/REVIEWER-GUIDELINES.html
N/A
- Rate the paper on a scale of 1-6, 6 being the strongest (6-4: accept; 3-1: reject). Please use the entire range of the distribution. Spreading the score helps create a distribution for decision-making.
(3) Weak Reject — marginally below the acceptance threshold, but would not mind if accepted, dependent on rebuttal
- Please justify your recommendation. What were the major factors that led you to your overall score for this paper?
While this method has promise, there is not enough evidence that it is clinically feasible or important, and the performance is not substantially better than other simpler, state-of-the-art methods.
- Reviewer confidence
Very confident (4)
- [Post rebuttal] After reading the authors’ rebuttal, please state your final opinion of the paper.
N/A
- [Post rebuttal] Please justify your final decision from above.
N/A
Review #3
- Please describe the contribution of the paper
The work introduced a multi-task framework, called PINet, that perform segmentation of infarct lesion and predict early neurological deterioration, with explicit cross-task feature interaction, that are critical for treatment planning. PINet is designed to address the issue of treating. By explicity considering inter-task information transfer in multi-task scenario, this work is promising for incorporating the evidence from the learned lesion structure into the classification modeling to ensure reliable clinical decision-making.
- Please list the major strengths of the paper: you should highlight a novel formulation, an original way to use data, demonstration of clinical feasibility, a novel application, a particularly strong evaluation, or anything else that is a strong aspect of this work. Please provide details, for instance, if a method is novel, explain what aspect is novel and why this is interesting.
The PINet framework is designed by 1) replacing average pooling in U-Net with a bijective discrete wavelet transform and inverse transform invertible scale transform that reduces spatial resolution while avoiding irreversible information loss, 2) proposing deep fusion method for task-specific fusion representation, 3) leveraging recursive modeling attribute of state-space models to capture long-range non-local dependencies, and 4) proposing a multi-modal second-order fusion classifier to enhance prediction by leveraging second-order statistics and modeling pairwise interactions between imaging and clinical features.
- Please list the major weaknesses of the paper. Please provide details: for instance, if you state that a formulation, way of using data, demonstration of clinical feasibility, or application is not novel, then you must provide specific references to prior work.
This work proposes an architecture built upon the U-Net framework. While the study demonstrates that hierarchical arrangement of architectural components can form a compounded design that enhances learning, the overall novelty appears limited. The primary contribution lies in the structural composition rather than in introducing fundamentally new methodological advances. The work lacks the classical mathematical rigor typically expected of a novel framework design. Instead, its strength is more evident in the implementation and training aspects of the model. As such, the rigor is reflected more in empirical training performance than in theoretical formulation. The authors may consider incorporating a Gaussian Process (GP) layer within the U-Net framework to enable smoothing and refinement, albeit at the cost of additional computational complexity, because GP scales poorly with large data. Additionally, the manuscript would benefit from greater transparency in the training process. Providing training trajectories, such as loss curves to illustrate the evolution of training and validation performance, would significantly strengthen the work by offering insights into convergence behavior and model generalization.
- Please rate the clarity and organization of this paper
Satisfactory
- Please comment on the reproducibility of the paper. Please be aware that providing code and data is a plus, but not a requirement for acceptance.
The submission has provided an anonymized link to the source code, dataset, or any other dependencies.
- Based on your review and your understanding of the MICCAI Scientific Code of Ethics, do you believe this submission may involve a potential ethics concern or violation?
N/A
- Optional: If you have any additional comments to share with the authors, please provide them here. Please also refer to our Reviewer’s guide on what makes a good review and pay specific attention to the different assessment criteria for the different paper categories: https://conferences.miccai.org/2026/en/REVIEWER-GUIDELINES.html
The authors should include a table defining all symbols and abbreviations, as the manuscript in its current form exhibits inconsistencies in notation. The authors should fix the repetition of references 24 in 25
- Rate the paper on a scale of 1-6, 6 being the strongest (6-4: accept; 3-1: reject). Please use the entire range of the distribution. Spreading the score helps create a distribution for decision-making.
(5) Accept — should be accepted, independent of rebuttal
- Please justify your recommendation. What were the major factors that led you to your overall score for this paper?
The paper can be revised, and the revised version would be suitable for publication. The authors may consider including a brief section titled “Remarks and Future Directions” to address the limitations identified in this work and to outline potential avenues for future research.
The authors demonstrate strong expertise in hierarchical architectural design and model training. However, future work would benefit from a greater emphasis on rigorous mathematical formulation and algorithmic development, rather than primarily showcasing architectural sophistication through coupling methods.
- Reviewer confidence
Confident but not absolutely certain (3)
- [Post rebuttal] After reading the authors’ rebuttal, please state your final opinion of the paper.
N/A
- [Post rebuttal] Please justify your final decision from above.
N/A
Author Feedback
We sincerely thank the reviewers and the Area Chair for their comments and questions, as well as for their acknowledgment of the significance of our work. Our responses are below:
R1-Q1: External validation and generalizability. We agree that multi-center external validation is important for assessing clinical robustness. The current study provides clinical validation based on 386 clinically collected cases. We will clarify this limitation in the final version and identify multi-center validation as an important future direction. R1-Q2: Computational complexity and deployment efficiency. We thank the reviewer for this valuable suggestion. We agree that computational efficiency is important for clinical deployment. In the final version, we will report relevant model complexity metrics. R1-Q3: Clinical interpretability. We agree that interpretability is highly important for the clinical application of END prediction. We will further investigate and discuss the clinical interpretability of the model in future work.
R2-Q1: Clinical importance and scope of pontine infarction. We thank the reviewer for this valuable suggestion. Although pontine infarction represents a subset of ischemic stroke, END in these patients can lead to severe functional deterioration and is clinically important for early monitoring and treatment planning. In future work, we will collaborate with partner hospitals to collect and evaluate data from more ischemic stroke subtypes to further validate and extend the clinical applicability of the framework. R2-Q2: Added complexity versus simpler baselines. We thank the reviewer for this important comment. Our ablation studies show that each component contributes to performance. In the final version, we will present these gains more clearly and clarify the advantages of our method over simpler pipelines. R2-Q3: Spearman correlations and clinical variables. We thank the reviewer for pointing this out. We have performed the Spearman correlation and clinical-variable analyses, and will clarify their use and related descriptions in the final version. R2-Q4: Data split and evaluation protocol. We thank the reviewer for this suggestion. We adopted 5-fold cross-validation to fully use the 386 cases and reduce bias from a single random split. For a medium-sized clinical dataset, this provides a more stable performance estimate. We will clarify the evaluation protocol in the final version. R2-Q5: Limitations and failure cases. We agree that a more transparent discussion of limitations and failure modes would strengthen the paper. We will briefly address this issue in the final version and provide a more systematic analysis of representative failure cases in future work.
R3-Q1: Novelty and methodological rigor. We thank the reviewer for the constructive comments. PINet is not a simple stacking of modules, but a carefully designed framework tailored to small pontine infarction segmentation and END prediction. We will clarify the methodological motivation and component roles in the final version. We also appreciate the GP-layer suggestion and will consider it in future work. R3-Q2: Transparency of the training process. We thank the reviewer for this valuable suggestion. We agree that training trajectories help demonstrate the model’s convergence behavior and optimization stability. We will include this analysis in future work. R3-Q3: Notation, abbreviations, and references. We thank the reviewer for pointing out these presentation issues. We will correct inconsistent notation, clarify necessary symbols and abbreviations, and fix the duplicated reference numbering in the final version. R3-Q4: Remarks and future directions. We thank the reviewer for this constructive suggestion. In the final version, we will provide a clearer explanation of the limitations and future research directions.
Meta-Review
Meta-review #1
- Your recommendation
Provisional Accept
- Please justify your decision. In case you deviate from the reviewers’ recommendations, explain in detail the reasons why. In case of an invitation for rebuttal, clarify which points are important to address in the rebuttal.
This paper presents PINet, a multimodal multi‑task framework for simultaneous pontine infarction segmentation and Early Neurological Deterioration (END) prediction. Two reviewers strongly support acceptance, recognizing its clinical value and technical novelty; one gives weak reject due to limited applicability and insufficient validation. I opt for Provisional Accept due to its technical novelty and clinical values.