Summary of Medmap: Promoting Incomplete Multi-modal Brain Tumor Segmentation with Alignment, by Tianyi Liu and Zhaorui Tan and Muyin Chen and Xi Yang and Haochuan Jiang and Kaizhu Huang
MedMAP: Promoting Incomplete Multi-modal Brain Tumor Segmentation with Alignment
by Tianyi Liu, Zhaorui Tan, Muyin Chen, Xi Yang, Haochuan Jiang, Kaizhu Huang
First submitted to arxiv on: 18 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the challenge of brain tumor segmentation in magnetic resonance imaging (MRI) when certain modalities are missing. Existing strategies, such as Knowledge Distillation, Domain Adaptation, and Shared Latent Space, typically overlook modality gaps, leading to limited performance for missing modality models. To overcome this limitation, the authors propose a novel paradigm that aligns latent features of involved modalities to a well-defined distribution anchor, mimicking the role of pre-trained models in natural visual segmentation tasks. This approach ensures a tight evidence lower bound, theoretically certifying its effectiveness. Extensive experiments on different backbones validate the proposed paradigm’s ability to enable invariant feature representations and produce models with narrowed modality gaps. The authors demonstrate superior performance on both BraTS2018 and BraTS2020 datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a way to make brain tumor segmentation in MRI work even when some of the imaging data is missing. Right now, doctors have to rely on multiple types of MRI scans to get an accurate picture of brain tumors. But what if one type of scan is missing? That makes it harder to identify the tumor accurately. To solve this problem, the authors came up with a new approach that helps models learn from different types of MRI scans and fill in the gaps when some data is missing. They tested their approach on real-world datasets and showed that it produces more accurate results than existing methods. |
Keywords
» Artificial intelligence » Domain adaptation » Knowledge distillation » Latent space
