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<link href="https://fonts.googleapis.com/css2?family=Source+Sans+Pro:wght@400;600;700&display=swap" rel="stylesheet" /> |
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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> |
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<h2 class="subtitle">Kalbe Digital Lab</h2> |
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<section class="overview"> |
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<h3 class="overview-heading"><span class="vl">Overview</span></h3> |
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<p class="overview-content"> |
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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> |
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<ul> |
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<li>Target: Capturing a broad spectrum of anatomies and lesions across different modalities.</li> |
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<li>Task: Segmentation</li> |
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<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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</ul> |
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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> |
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<img class="content-image" src="file/figures/medsam.png" alt="model-architecture" /> |
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<h3 class="overview-heading"><span class="vl">Demo</span></h3> |
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<p class="overview-content">Please select the example below or upload 2 pairs of mammography exam result.</p> |
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