{
    "abstract": "Automatic segmentation in Breast Ultrasound (BUS) imaging is vital to BUS computer-aided diagnostic systems. Fully supervised learning approaches can attain high accuracy, yet they depend on pixel-level annotations that are challenging to obtain. As an alternative, weakly supervised learning methods offer a way to lessen the dependency on extensive annotation requirements. Existing weakly supervised learning methods are typically trained on the entire dataset, but not all samples are effective in training a robust image segmentation model. To overcome this challenge, we have developed a new weakly supervised learning approach for BUS image segmentation. Our framework includes three key contributions: 1) A novel image selection method using Class Activation Maps is proposed to identify high-quality candidates for generating pseudo-segmentation labels; 2) The 'Segment Anything' is utilized for pseudo-label generation; 3) A segmentation model is trained using a Mean Teacher method, incorporating both pseudo-labeled and non-labeled images. The proposed framework is evaluated on a public BUS image dataset and achieves an Intersection over Union score that is 82.9% of what is attained by fully supervised methods.",
    "authors": [
        {
            "affiliation": null,
            "firstname": "Tzu-Han",
            "initials": "TH",
            "lastname": "Lin"
        },
        {
            "affiliation": null,
            "firstname": "Daehan",
            "initials": "D",
            "lastname": "Kwak"
        },
        {
            "affiliation": null,
            "firstname": "Kuan",
            "initials": "K",
            "lastname": "Huang"
        }
    ],
    "conclusions": null,
    "copyrights": null,
    "doi": "10.1109/EMBC53108.2024.10781719",
    "journal": "Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference",
    "keywords": [],
    "methods": null,
    "publication_date": "2025-03-05",
    "pubmed_id": "40039861",
    "results": null,
    "title": "Weakly Supervised Breast Ultrasound Image Segmentation Based on Image Selection.",
    "xml": "<Element 'PubmedArticle' at 0x788506ad16c0>"
}