Personalize to generalize: Towards a universal medical multi-modality generalization through personalization
Medical modalities;multi-modality generalization;personalized medicine
Deanonymizing information (author's name) in the source code zip file provided
Submission Desk Rejected by Program Chairs
@misc{
tan2024personalize,
title={Personalize to generalize: Towards a universal medical multi-modality generalization through personalization},
author={Zhaorui Tan and Xi Yang and Tan Pan and TIANYI LIU and Chen Jiang and Xin Guo and Qiufeng Wang and Anh Nguyen and Yuan Qi and Kaizhu Huang and Yuan Cheng},
year={2024},
url={https://openreview.net/forum?id=nQoRKLeOP0}
}
Personalized medicine is a groundbreaking healthcare framework for the $21^{st}$ century, tailoring medical treatments to individuals based on unique clinical characteristics, including diverse medical imaging modalities. These modalities differ significantly due to distinct underlying imaging principles, creating substantial challenges for generalization in multi-modal medical image tasks. Previous methods addressing multi-modal generalization rarely consider personalization, primarily focusing on common anatomical information. This paper aims to connect multi-modal generalization with the concept of personalized medicine. Specifically, we propose a novel approach to derive a tractable form of the underlying personalized invariant representation $\mathbb{X}_h$ using individual-level constraints and a learnable biological prior. We demonstrate that learning a personalized $\mathbb{X}_h$ is both feasible and beneficial, as this representation proves highly generalizable and transferable across various multi-modal medical tasks. Our method is rigorously validated on medical imaging modalities emphasizing both physical structure and functional information, encompassing a range of tasks that require generalization. Extensive experimental results consistently show that our approach significantly improves performance across diverse scenarios, confirming its effectiveness.
I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
~Zhaorui_Tan1 ~Xi_Yang1 ~Tan_Pan1 ~TIANYI_LIU9 ~Chen_Jiang2 ~Xin_Guo2 ~Qiufeng_Wang2 ~Anh_Nguyen2 ~Yuan_Qi2 ~Kaizhu_Huang1 ~Yuan_Cheng1
Zhaorui Tan Xi Yang Tan Pan TIANYI LIU Chen Jiang Xin Guo Qiufeng Wang Anh Nguyen Yuan Qi Kaizhu Huang Yuan Cheng
I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
Medical modalities multi-modality generalization personalized medicine
I certify that there is no acknowledgement section in this submission for double blind review.
tan|personalize_to_generalize_towards_a_universal_medical_multimodality_generalization_through_personalization
/pdf/75ee9ce4a84131aa3abdfeb66c3bbef07a7a3876.pdf
applications to computer vision, audio, language, and other modalities
I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6.
I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.
/attachment/1928d79ba18f38be2d841392ea05f59a16b0155b.zip
Personalize to generalize: Towards a universal medical multi-modality generalization through personalization
ICLR 2025 Conference Desk Rejected Submission
ICLR.cc/2025/Conference/Desk_Rejected_Submission