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| <title>Visual Question Answering (VQA) for Medical Imaging</title> | |
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| <h1 class="title">Visual Question Answering (VQA) for Medical Imaging</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> | |
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| The project addresses the challenge of accurate and efficient medical imaging analysis in healthcare, aiming to reduce human error and workload for radiologists. | |
| The proposed solution involves developing advanced AI models for Visual Question Answering (VQA) to assist healthcare professionals in analyzing medical images quickly and accurately. | |
| These models will be integrated into a user-friendly web application, providing a practical tool for real-world healthcare settings. | |
| </p> | |
| <p class="overview-content">References: <a href="https://www.sciencedirect.com/science/article/abs/pii/S0933365723001252" target="_blank">https://www.sciencedirect.com/science/article/abs/pii/S0933365723001252</a></p> | |
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| <h3 class="overview-heading"><span class="vl">Dataset</span></h3> | |
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| The model is trained with Colorectal Nuclear Segmentation and Phenotypes (CoNSeP) dataset | |
| <a href="https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet" target="_blank">https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet</a>. Images were extracted from 16 colorectal adenocarcinoma (CRA) WSIs. | |
| </p> | |
| <ul> | |
| <li>Target: Nuclei</li> | |
| <li>Task: Classification</li> | |
| <li>Modality: Images (Histology and Label) </li> | |
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| <h3 class="overview-heading"><span class="vl">Model Architecture</span></h3> | |
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| <p class="overview-content">The model is trained using DenseNet121 over CoNSep dataset.</p> | |
| <img class="content-image" src="img/Model-Architecture.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 or upload a nuclei histology image and label image to see Nuclei Cells Classification capabilities of this model</p> | |
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