TY - GEN
T1 - Mind the Gap
T2 - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024
AU - Liu, Tianyi
AU - Tan, Zhaorui
AU - Jiang, Haochuan
AU - Yang, Xi
AU - Huang, Kaizhu
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Brain tumor segmentation is often based on multiple magnetic resonance imaging (MRI). However, in clinical practice, certain modalities of MRI may be missing, which presents an even more difficult scenario. To cope with this challenge, knowledge distillation has emerged as one promising strategy. However, recent efforts typically overlook the modality gaps and thus fail to learn invariant feature representations across different modalities. Such drawback consequently leads to limited performance for both teachers and students. To ameliorate these problems, in this paper, we propose a novel paradigm that aligns latent features of involved modalities to a well-defined distribution anchor. As a major contribution, we prove that our novel training paradigm ensures a tight evidence lower bound, thus theoretically certifying its effectiveness. Extensive experiments on different backbones validate that the proposed paradigm can enable invariant feature representations and produce a teacher with narrowed modality gaps. This further offers superior guidance for missing modality students, achieving an average improvement of 1.75 on dice score.
AB - Brain tumor segmentation is often based on multiple magnetic resonance imaging (MRI). However, in clinical practice, certain modalities of MRI may be missing, which presents an even more difficult scenario. To cope with this challenge, knowledge distillation has emerged as one promising strategy. However, recent efforts typically overlook the modality gaps and thus fail to learn invariant feature representations across different modalities. Such drawback consequently leads to limited performance for both teachers and students. To ameliorate these problems, in this paper, we propose a novel paradigm that aligns latent features of involved modalities to a well-defined distribution anchor. As a major contribution, we prove that our novel training paradigm ensures a tight evidence lower bound, thus theoretically certifying its effectiveness. Extensive experiments on different backbones validate that the proposed paradigm can enable invariant feature representations and produce a teacher with narrowed modality gaps. This further offers superior guidance for missing modality students, achieving an average improvement of 1.75 on dice score.
KW - Alignment
KW - Brain Tumor Segmentation
KW - Knowledge Distillation
KW - Missing Modality
UR - https://www.scopus.com/pages/publications/85217277330
U2 - 10.1109/BIBM62325.2024.10822387
DO - 10.1109/BIBM62325.2024.10822387
M3 - Conference Proceeding
AN - SCOPUS:85217277330
T3 - Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024
SP - 3531
EP - 3536
BT - Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024
A2 - Cannataro, Mario
A2 - Zheng, Huiru
A2 - Gao, Lin
A2 - Cheng, Jianlin
A2 - de Miranda, Joao Luis
A2 - Zumpano, Ester
A2 - Hu, Xiaohua
A2 - Cho, Young-Rae
A2 - Park, Taesung
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 3 December 2024 through 6 December 2024
ER -