Summary of Intrapartum Ultrasound Image Segmentation Of Pubic Symphysis and Fetal Head Using Dual Student-teacher Framework with Cnn-vit Collaborative Learning, by Jianmei Jiang et al.
Intrapartum Ultrasound Image Segmentation of Pubic Symphysis and Fetal Head Using Dual Student-Teacher Framework with CNN-ViT Collaborative Learning
by Jianmei Jiang, Huijin Wang, Jieyun Bai, Shun Long, Shuangping Chen, Victor M. Campello, Karim Lekadir
First submitted to arxiv on: 11 Sep 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 The proposed Dual-Student and Teacher Combining CNN and Transformer (DSTCT) framework synergistically integrates Convolutional Neural Networks (CNNs) and Transformers for pubic symphysis and fetal head (PSFH) segmentation in ultrasound images. The framework comprises a Vision Transformer (ViT) as the teacher and two student models, one ViT and one CNN, which mutually supervise each other through the generation of pseudo-labels. The teacher model reinforces learning by imposing consistency regularization constraints. To enhance generalization, data and model perturbation techniques are employed. The DSTCT framework outperforms ten contemporary semi-supervised segmentation methods on the PSFH Segmentation Grand Challenge at MICCAI 2023. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to improve computer vision models that can analyze ultrasound images. These models help doctors monitor labor and detect potential complications during childbirth. The researchers used special types of artificial intelligence called Convolutional Neural Networks (CNNs) and Transformers to develop their model. They combined these approaches in a unique way, creating a “teacher” and two “student” models that work together to learn from the data. This approach helps the model learn more accurately from small amounts of labeled data and large amounts of unlabeled data. The new method was tested on a challenging dataset and outperformed other similar methods. |
Keywords
» Artificial intelligence » Cnn » Generalization » Regularization » Semi supervised » Teacher model » Transformer » Vision transformer » Vit
