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| <title>Bilateral View Hypercomplex Breast Classification</title> | |
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| <h1 class="title">MedSAM: Segment Anything in Medical Images</h1> | |
| <h2 class="subtitle">Kalbe Digital Lab</h2> | |
| <section class="overview"> | |
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| <h3 class="overview-heading"><span class="vl">Overview</span></h3> | |
| <p class="overview-content"> | |
| MedSAM, a foundation model for universal medical image segmentation. MedSAM is adapted from the SAM model on an unprecedented scale, with more than one million medical image-mask pairs. | |
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| Reference: <a href="https://arxiv.org/abs/2304.12306" target="_blank">https://arxiv.org/abs/2204.05798</a> | |
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| <h3 class="overview-heading"><span class="vl">Dataset</span></h3> | |
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| <p class="overview-content">The model is trained using a diverse and large-scale medical image segmentation dataset with 1,090,486 medical image-mask pairs, covering 15 imaging modalities, over 30 cancer types, and a multitude of imaging protocols.</p> | |
| <ul> | |
| <li>Target: Capturing a broad spectrum of anatomies and lesions across different modalities.</li> | |
| <li>Task: Segmentation</li> | |
| <li>Modality: Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Endoscopy, Ultrasound, Pathology, Fundus, Dermoscopy, Mammography, and Optical Coherence Tomography (OCT).</li> | |
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| <h3 class="overview-heading"><span class="vl">Model Architecture</span></h3> | |
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| <p class="overview-content">Parameterized Hypercomplex ResNets-18 Variants (PHYResNet).</p> | |
| <img class="content-image" src="file/figures/medsam.png" alt="model-architecture" /> | |
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| </section> | |
| <h3 class="overview-heading"><span class="vl">Demo</span></h3> | |
| <p class="overview-content">Please select the example below or upload 2 pairs of mammography exam result.</p> | |
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