Summary of Breast Tumor Classification Based on Self-supervised Contrastive Learning From Ultrasound Videos, by Yunxin Tang et al.
Breast tumor classification based on self-supervised contrastive learning from ultrasound videos
by Yunxin Tang, Siyuan Tang, Jian Zhang, Hao Chen
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper proposes a self-supervised contrastive learning method for training deep learning models to diagnose breast tumors from ultrasound videos, without requiring large amounts of labeled data. The approach uses a triplet network with a novel hard triplet loss function to learn representations that discriminate between positive and negative image pairs. The authors construct two datasets: a pretraining dataset with 12,000+ images and a finetuning dataset with 400 images. They train their model using contrastive learning and evaluate its performance on a benign/malignant classification task, achieving an AUC of 0.952 compared to state-of-the-art models. The results show that the proposed framework can learn effective representations from limited labeled data, reducing the need for expensive professional annotations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using artificial intelligence (AI) to help doctors diagnose breast tumors from ultrasound videos. Currently, AI systems are not very good at this task because they need a lot of data to be trained, and getting that data can be time-consuming and expensive. The researchers in this paper came up with a new way to train their AI model using ultrasound videos without needing as much labeled data. They tested their approach and found that it worked really well, even better than some other state-of-the-art models. This is important because it could make it easier for doctors to use AI to diagnose breast tumors in the future. |
Keywords
» Artificial intelligence » Auc » Classification » Deep learning » Pretraining » Self supervised » Triplet loss
