Spaces:
Runtime error
Runtime error
| <html> | |
| <head> | |
| <link rel="stylesheet" href="file/style.css" /> | |
| <link rel="preconnect" href="https://fonts.googleapis.com" /> | |
| <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin /> | |
| <link href="https://fonts.googleapis.com/css2?family=Source+Sans+Pro:wght@400;600;700&display=swap" rel="stylesheet" /> | |
| <title><strong>Body Part Classification</strong></title> | |
| </head> | |
| <body> | |
| <div class="container"> | |
| <h1 class="title"><strong> Body Part Classification</strong></h1> | |
| <h2 class="subtitle"><strong>Kalbe Digital Lab</strong></h2> | |
| <section class="overview"> | |
| <div class="grid-container"> | |
| <h3 class="overview-heading"><span class="vl">Overview</span></h3> | |
| <p class="overview-content"> | |
| The Body Part Classification program serves the critical purpose of categorizing body parts from DICOM x-ray scans into five distinct classes: abdominal, adult chest, pediatric chest, spine, and others. This program trained using ResNet18 model. | |
| </p> | |
| </div> | |
| <div class="grid-container"> | |
| <h3 class="overview-heading"><span class="vl">Dataset</span></h3> | |
| <div> | |
| <p class="overview-content"> | |
| The program has been meticulously trained on a robust and diverse dataset, specifically <a href="https://vindr.ai/datasets/bodypartxr" target="_blank">VinDrBodyPartXR Dataset.</a>. | |
| <br/> | |
| This dataset is introduced by Vingroup of Big Data Institute which include 16,093 x-ray images that are collected and manually annotated. It is a highly valuable resource that has been instrumental in the training of our model. | |
| </p> | |
| <ul> | |
| <li>Objective: Body Part Identification</li> | |
| <li>Task: Classification</li> | |
| <li>Modality: Grayscale Images</li> | |
| </ul> | |
| </div> | |
| </div> | |
| <div class="grid-container"> | |
| <h3 class="overview-heading"><span class="vl">Model Architecture</span></h3> | |
| <div> | |
| <p class="overview-content"> | |
| The model architecture of ResNet18 to train x-ray images for classifying body part. | |
| </p> | |
| <img class="content-image" src="file/figures/ResNet-18.png" alt="model-architecture" width="425" height="115" style="vertical-align:middle" /> | |
| </div> | |
| </div> | |
| </section> | |
| <h3 class="overview-heading"><span class="vl">Demo</span></h3> | |
| <p class="overview-content">Please select or upload a body part x-ray scan image to see the capabilities of body part classification with this model</p> | |
| </div> | |
| </body> | |
| </html> |