The medical imaging industry has always built very high field barriers based on data and high-cost annotation. With the recent introduction of general CV large models such as SAM and SegGPT, large-scale Models and general models are also gradually burning in the field of CV, especially in the field of image segmentation. Various general medical image segmentation models have also emerged .
On 11.16-11.17, we invited Ph.D. from Taiwan Chiao Tung University, author of many top articles, Mr. Shawn< a i=3>, brings us - New SOTA for general cross-modal medical image segmentation, and explains the medical image segmentation task in detail.
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MICCAI2023 popular medical imaging paper collection
Teacher introduction: Teacher Shawn
-Ph.D., Taiwan Chiao Tung University
-Published multiple papers as the first author, including ICLR, ICDE
-Won multiple school-level scholarships, AI competitions, and cooperated with the Singapore Ministry of Science and Technology
-Research directions: deep learning, computer vision, music generation, multi-modality
Live broadcast outline
1) For medical image segmentation, if a model is trained using only MR images in the source domain, how good is its performance in directly segmenting CT images in the target domain?
2) For generalized medical image segmentation tasks, it is very difficult to train a model using a single source domain. How to solve this situation?
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For medical image segmentation, imagine if a model is trained using only MR images in the source domain, how good is its performance in directly segmenting CT images in the target domain? This setup, i.e., generalizable cross-modal segmentation, has clinical potential , more challenging than other related settings such as domain adaptation.
For generalized medical image segmentation tasks, it is very difficult to train a model using a single source domain. Stylistic deviations between different modes can significantly degrade performance. As shown in the figure below, different modalities of brain tumor impact have significantly different appearances, as shown in the figure below, T1 and T2. Existing models often only perform well in a single modality and perform poorly in another modality, such as DeepAll and DoFE methods.
Recently, some research has proposedSAM-Med3D, which is a three-dimensional SAM model specially used for 3D voxel medical image segmentation. . This model significantly outperforms other methods while providing limited cue points for different anatomical structures such as bones, heart, and muscles. Under different image modalities, especially MRI images, more cue points are usually required to achieve the same performance than CT images, but SAM-Med3D performs well under various modalities (including MRI images), organs, and lesions. Always perform well. In addition, the transferability of SAM-Med3D has also been verified on different benchmark tasks, and the model has shown strong potential, so SAM-Med3D is expected to become a powerful pre-training model for 3D medical image Transformer.
SAM-Med3D with complete 3D structure
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As a novice in scientific research, how can I publish a high-quality paper?
For the paper, everyone is working hard to design new networks, new strategies, and new training algorithms. As long as we can achieve a good performance on a certain problem, the paper will be completed naturally. And if you want to achieve it quickly, guidance from seniors is indispensable.
The role of a good instructor is that if there is no topic, he can help you plan the topic based on the specific situation of the topic group and recent popular research directions. If there is a topic but lacks innovative directions, the teacher can quickly help you find several entry points. , several frameworks, and even help you think about which documents you need to read...
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MICCAI2023 popular medical imaging paper collection
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