Lambda/gallery.html at master · Yash5044/Lambda · GitHub

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<!DOCTYPE html>
<html lang="en">
<head>

<meta charset="UTF-8">
<meta http-equiv="X-UA-Compatible" content="IE=Edge">
<meta name="description" content="">
<meta name="keywords" content="">
<meta name="author" content="">
<meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1">

<title>Gallery</title>
<link rel="stylesheet" href="css/bootstrap.min.css">
<link rel="stylesheet" href="css/font-awesome.min.css">
<link rel="stylesheet" href="css/magnific-popup.css">

<!-- Main css -->
<link rel="stylesheet" href="css/style.css">
<link href="https://fonts.googleapis.com/css?family=Lora|Merriweather:300,400" rel="stylesheet">

</head>
<body>

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<div class="preloader">
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<div class="navbar navbar-default navbar-static-top" role="navigation">
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               <a href="index.html" class="navbar-brand">Lambda</a>
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                    <li><a href="index.html">Home</a></li>
                    <li><a href="about.html">Members</a></li>
                    <li class="active"><a href="gallery.html">Projects</a></li>
                    <li><a href="contact.html">Contact</a></li>
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<section id="home" class="main-gallery parallax-section">
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     <div class="container">
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               <div class="col-md-12 col-sm-12">
                    <h1>Projects</h1>
               </div>

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</section>

<!-- Gallery Section -->

<section id="gallery">
     <div class="container">
          <div class="row">

               <div class="col-md-offset-1 col-md-10 col-sm-12">
                    <h2>SKIN LESION ANALYSIS FOR MELANOMA DETECTION : </h2>
<img src="./images/one.png" height="300" width = "300" align="right">
                    <ul><li>
<p>Proposed a state of the art segmentation network for classify lesion pixel in demroscopic images.</p></li>
                    <li><p>A two stage architecture first localizes the possible skin lesion and then are fed to the segmentation network.</p></li>
<li><p>Segmentation network was pretrained on the cropped dataset prepared from the original masks.
</p></li>
<p>Performed by - Shreshth Saini & Divij Gupta</p>
</ul></div>

           <div class="col-md-offset-1 col-md-10 col-sm-12">

                    <h2>SEGMENTATION OF THE LEFT ATRIAL CAVITY FROM 3D GADOLINIUM-ENHANCED MRI DATA : </h2>
<img src="./images/two.png" height="300" width = "300" align="right">
                    <ul><li>
<p>We propose a 3D architecture for the localization of the 3D data to produce tightly fit 3D volumetric samples.</p></li>
                    <li><p>A modified version of the Faster RCNN based architecture is developed for the localization and producing cubic samples.</p></li>
<li><p>The cubes are then fed to a 3D U-Net + Hourglass network for generating 3D masks
</p></li>
<p>Performed by - Shreshth Saini & Divij Gupta</p>
</ul></div>

<div class="col-md-offset-1 col-md-10 col-sm-12">
                    <h2>EAR SEGMENTATION : </h2>
<img src="./images/three.png" height="300" width = "300" align="right">
                    <ul><li>
<p>An end-to-end architecture to segment the human-Ear was designed in keras.</p></li>
                    <li><p>The architecture was inspired from UNet and was modified intensively and performed well.</p></li>
<li><p>Further work introduces the pix2pix GANs for the segmentation task.
</p></li>
<p>Performed by - Shreshth Saini & Divij Gupta</p>
</ul></div>

<div class="col-md-offset-1 col-md-10 col-sm-12">

                    <h2>Classification of PathologicAL Myopia from retinal images using Deep Learning Network : </h2>
<img src="./images/image.png" height="300" width = "300" align="right">
            <ul>
<p>This project focuses on investigation and development of an algorithms associated with recognition of Pathologic Myopia (PM) and classification of fundus images taken from patients suffered from PM disease into 3 labels:</p>
<li><p>Pathological Myopia</p></li>  <li><p>High Myopia</p></li>
<p>Myopia is one of the optical disease having highest morbidity. More than 2 billion people have myopia in whole world, among which 35% are suffering from high myopia, it is important to have early diagnosis and regular follow-up. This has motivated the research and development of Classification network so that it can be detected at early stage and can be cured.   </p>
<p>Performed by - Anupama Patel</p>
</ul></div>

<div class="col-md-offset-1 col-md-10 col-sm-12">
                    <h2>CLASSIFICATION OF DOCUMENT EMBEDDED IMAGES : </h2>
                    <ul><li>
<p>Our architecture had RES-NET50 as feature extractor and a Fully connected classifier
classifying into 6 classes.</p></li>
                    <li><p>Made our own data-set consisting of 1050 images of various Document Embedded
images of class Tables, Graphs, Charts, etc</p></li>
<p>Performed by - Mayank Maheshwari</p>
<p>GUIDE - DR. GAURAV HARIT</p>

</ul></div>

<div class="col-md-offset-1 col-md-10 col-sm-12">
                    <h2>SPIE-AAPM-NCI BREASTPATHQ: CANCER CELLULARITY CHALLENGE 2019

 : </h2>
<p>Performed by - Nisarg Shah</p>
                   </div>

<div class="col-md-offset-1 col-md-10 col-sm-12">
                    <h2>CHAOS - COMBINED (CT-MR) HEALTHY ABDOMINAL ORGAN SEGMENTATION
 : </h2>
<p>Performed by - Nisarg Shah</p>
                   </div>


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                    <h3>Lambda</h3>
                    <p>We are a group of undergraduate students who work on solving real problems through Artificial Intelligence.</p>
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                    <p><i class="fa fa-globe"></i> Indian Institute of Technology, Jodhpur</p>
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