-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathinternship-old.html
More file actions
354 lines (300 loc) · 16.6 KB
/
Copy pathinternship-old.html
File metadata and controls
354 lines (300 loc) · 16.6 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
<!DOCTYPE html>
<html lang="en">
<!-- ======= Head Section - DON'T CHANGE ======= -->
<head>
<meta charset="utf-8">
<meta content="width=device-width, initial-scale=1.0" name="viewport">
<title> Internship_Projects</title>
<link href="assets/img/logo.png" rel="icon">
<!-- Google Fonts -->
<link href="https://fonts.googleapis.com/css?family=Open+Sans:300,300i,400,400i,600,600i,700,700i|Raleway:300,300i,400,400i,500,500i,600,600i,700,700i|Poppins:300,300i,400,400i,500,500i,600,600i,700,700i" rel="stylesheet">
<!-- Vendor CSS Files -->
<link href="assets/vendor/aos/aos.css" rel="stylesheet">
<link href="assets/vendor/bootstrap/css/bootstrap.min.css" rel="stylesheet">
<link href="assets/vendor/bootstrap-icons/bootstrap-icons.css" rel="stylesheet">
<link href="assets/vendor/boxicons/css/boxicons.min.css" rel="stylesheet">
<link href="assets/vendor/glightbox/css/glightbox.min.css" rel="stylesheet">
<link href="assets/vendor/remixicon/remixicon.css" rel="stylesheet">
<link href="assets/vendor/swiper/swiper-bundle.min.css" rel="stylesheet">
<!-- Main CSS File -->
<link href="assets/css/style.css" rel="stylesheet">
</head>
<!-- ======= Body Section ======= -->
<body>
<!-- ======= Navigation Bar - DON'T CHANGE ======= -->
<header id="header" class="fixed-top">
<div class="container d-flex align-items-center justify-content-between">
<a href="index.html" class="logo"><img src="assets/img/logo.png" alt="" class="img-fluid"></a>
<nav id="navbar" class="navbar">
<ul>
<li><a class="nav-link scrollto" href="index.html">Home</a></li>
<li><a class="nav-link scrollto" href="https://intsav.github.io/index.html#footer">Contact</a></li>
</ul>
<i class="bi bi-list mobile-nav-toggle"></i>
</nav>
</div>
</header><!-- End Navigation Bar -->
<!-- ======= Title Section ======= -->
<section id="project-title" class="d-flex align-items-center">
<div class="container position-relative" data-aos="fade-up" data-aos-delay="100">
<div class="row">
<div class="col-12 text-center">
<h1>Internship Projects</h1>
</div>
</div>
</div>
</section><!-- End Title -->
<!-- Main Webpage Section -->
<main id="project-main">
<!-- ======= Project Body Section ======= -->
<section id="project-body">
<!-- ======= Project Introduction ======= -->
<div class="project-intro">
<div class="container">
<div class="row justify-content-left">
<div class="col-12">
<hr>
<h1>Applicants are invited for several Graduate Research Assistant positions to undertake
research and development for innovative projects.<br> All projects will
focus on the implementation of state-of-the-art AI techniques in healthcare applications.</h1>
<br>
</div>
</div>
<!-- <div class="text-left">
<a class="btn btn-primary" href="https://www.sciencedirect.com/science/article/abs/pii/S0010482521001670" role="button">Download the paper</a>
</div> -->
</div>
</div>
<!-- ======= Project 1 ======= -->
<div class = "project-1">
<div class="container project-container">
<div class="row justify-content-left d-flex flex-wrap align-items-center">
<div class="row justify-content-left">
<div class="col-12">
<div align="justify">
<h2 style="color:blue;"><b>Role 1: Automated Early Detection of Barrett’s Esophagus (BE) in Endoscopic Images</b></h2> <br>
<h3>
Barrett's Esophagus (BE) is a condition where the normal lining of the esophagus is replaced by
abnormal tissue, increasing the risk of esophageal cancer. <br>The early detection of BE is crucial
for timely treatment and improved patient outcomes. However, the current method of diagnosing
BE is through endoscopy, which is an invasive and time-consuming procedure.
<br>Moreover, the visual assessment of endoscopic images for BE detection is
subjective and prone to inter-observer variability.
</h3>
<div align="center">
<br><br>
<img class="img-fluid" src="assets\img\projects\Portfolio\Internship\Role1.png" alt="IMAGE Dataset">
</div>
<div align="justify">
<h3>
<br><br>To overcome these limitations, there is a need for an automated system that can detect BE in
endoscopic images accurately and efficiently. The aim of this project is to develop an
automated early detection system for BE in endoscopic images using machine learning algorithms.<br><br>
The project will involve the following steps:<br><br>
<ul>
<li>Data Preprocessing to remove noise and artifacts, and to enhance the contrast and sharpness of the images.
This step will improve the quality of the images and ensure better detection accuracy.
</li>
<li>Develop a deep learning Model for automatic classification of the endoscopic images as BE or non-BE.
</li>
<li>Evaluation the performance of the developed model on test set of endoscopic images using evaluation metrics.
</li>
<li>Deploy the developed model as an automated system for early detection of BE in endoscopic images.
</li>
</ul>
<br>
The ultimate goal of the project is to provide a cost-effective, non-invasive and reliable screening tool for the early detection of BE, enabling prompt treatment and improving patient outcomes.
This system will help improve the diagnosis of BE and reduce the burden on endoscopists.
</h3>
<br>
<h3> <a href="assets\img\projects\Portfolio\Internship\Graduate Research Assistant in Artificial Intelligence_SCE1_ role_1.pdf" target="_blank" rel="noopener noreferrer"> Download the document</a></h3>
</div>
</div>
</div>
</div>
</div>
<hr>
</div>
</div>
<!-- ======= Project 2 ======= -->
<div class = "project-2">
<div class="container project-container">
<div class="row justify-content-left d-flex flex-wrap align-items-center">
<div class="row justify-content-left">
<div class="col-12">
<div align="justify">
<h2 style="color:blue;"><b>Role 2: Automated Polyp detection and segmentation in Colonoscopy Images</b></h2> <br>
<h3>
Colorectal cancer is the third most common cancer worldwide, with about 1.8 million new
cases and 900,000 deaths per year. Screening for colorectal cancer is essential for early
detection and prevention, and colonoscopy is the most effective screening method. However, the
effectiveness of colonoscopy depends on the ability of the endoscopist to detect and remove polyps,
which are the precursor lesions to colorectal cancer.
Missed polyps can lead to delayed diagnosis and treatment, and therefore,
improving polyp detection rates is crucial for reducing the incidence and mortality of colorectal cancer.
</h3>
<div align="center">
<br><br>
<img class="img-fluid" src="assets\img\projects\Portfolio\Internship\Role2.png" alt="IMAGE Dataset">
</div>
<div align="justify">
<h3>
<br><br>The objective of this project is to improve polyp detection rates in colonoscopy and,
consequently, improve the screening and diagnosis of colorectal cancer. <br><br>
The project aims to achieve the following objectives:<br><br>
<ul>
<li>Develop a deep learning algorithm for automatic polyp detection and classification in
colonoscopy images. </li>
<li>Train and validate the algorithm using a large dataset of colonoscopy images with polyps of different sizes, shapes,
and locations. </li>
<li>Integrate the algorithm into the colonoscopy system to assist endoscopists in
real-time during the procedure.</li>
<li>Evaluate the performance of the algorithm in terms of sensitivity, specificity, and positive predictive value,
and compare it with the performance of human endoscopists.</li>
<li>Assess the impact of the algorithm on polyp detection rates, adenoma detection rates, and interval cancer rates in a clinical setting.
</li>
</ul>
</h3>
<br>
<h3> <a href="assets\img\projects\Portfolio\Internship\Graduate Research Assistant in Artificial Intelligence_SCE1_ role_2.pdf" target="_blank" rel="noopener noreferrer"> Download the document</a></h3>
</div>
</div>
</div>
</div>
</div>
<hr>
</div>
</div>
<!-- ======= Project 3 ======= -->
<div class = "project-3">
<div class="container project-container">
<div class="row justify-content-left d-flex flex-wrap align-items-center">
<div class="row justify-content-left">
<div class="col-12">
<div align="justify">
<h2 style="color:blue;"><b>Role 3: Doppler Mitral inflow echocardiographic_ pixel to velocity</b></h2> <br>
<h3>
This project focuses on the development of a deployable model for converting pixel
values to velocity values in Doppler Mitral inflow echocardiographic images. Currently,
AI models can detect and measure necessary parameters on Mitral echocardiographic images,
but the measurements are acquired in pixel values. In order to adapt these techniques to real-world
scenarios,
automated measurements should be converted to velocity values.
</h3>
<div align="center">
<br><br>
<img class="img-fluid" src="assets\img\projects\Portfolio\Internship\Role3.png" alt="IMAGE Dataset">
</div>
<div align="justify">
<h3>
<br><br>The objective of this project is to develop a deployable model that can process
echocardiographic images and determine the pixel-to-velocity conversion rate. The model will
be developed using computer vision techniques and will be incorporated into a larger deep
learning pipeline. <br><br>
Expected outcomes: The deployable model developed in this project will enable automated
pixel-to-velocity conversion in Doppler Mitral inflow echocardiographic images.
This will facilitate the translation of AI models for functional assessment of the
Mitral valve into real-world scenarios. The project will contribute to the advancement
of non-invasive cardiac diagnostics, leading to better patient care and outcomes.
</h3>
<br>
<h3> <a href="assets\img\projects\Portfolio\Internship\Graduate Research Assistant in Artificial Intelligence_SCE1_ role_3.pdf" target="_blank" rel="noopener noreferrer"> Download the document</a></h3>
</div>
</div>
</div>
</div>
</div>
<hr>
</div>
</div>
<!-- ======= Project 4 ======= -->
<div class = "project-4">
<div class="container project-container">
<div class="row justify-content-left d-flex flex-wrap align-items-center">
<div class="row justify-content-left">
<div class="col-12">
<div align="justify">
<h2 style="color:blue;"><b>Role 4: Doppler Flow Imaging and Analysis</b></h2> <br>
<h3>
Doppler echocardiography is a widely used technique in the functional evaluation of heart valves.
However, the current measurement process is laborious and subjective, leading to high intra- and
inter-observer variability. The use of automated systems to analyse Doppler echocardiographic images
could standardise the measurement process and potentially
reduce the time spent on such measurements, leading to improved clinical workflow.
</h3>
<div align="center">
<br><br>
<img class="img-fluid" src="assets\img\projects\Portfolio\Internship\Role4.png" alt="IMAGE Dataset">
</div>
<div align="justify">
<h3>
<br><br>The objective of this project is to develop and evaluate an automated system for Doppler
echocardiography analysis. The system will utilise
neural networks to automate the measurement process and produce accurate and reproducible results. <br><br>
Expected outcomes: The automated system for Doppler echocardiography analysis is expected to provide a
standardised and efficient method for functional evaluation of the heart. The system will reduce the
subjectivity and variability associated with manual measurements, resulting in more accurate and
reproducible results. <br>
The system will also potentially improve clinical workflow by reducing the time and effort required for analysis.
</h3>
<br>
<h3> <a href="assets\img\projects\Portfolio\Internship\Graduate Research Assistant in Artificial Intelligence_SCE4_role_4.pdf" target="_blank" rel="noopener noreferrer"> Download the document</a></h3>
</div>
</div>
</div>
</div>
</div>
<hr>
</div>
</div>
</section><!-- End project-body -->
</main><!-- End #main -->
<!-- ======= Footer - DONT CHANGE ======= -->
<footer id="footer">
<div class="footer-top">
<div class="container">
<div class="section-title">
<h2>Contact Details</h2>
</div>
<div class="row">
<div class="col-lg-3 col-md-6 footer-contact">
<p>
Professor Massoud Zolgharni <br>
Massoud.Zolgharni@uwl.ac.uk<br>
</p>
</div>
<div class="col-lg-2 col-md-6 footer-links">
<p>
Dr Nasim Dadashi <br>
nasim.dadashiserej@uwl.ac.uk<br>
</p>
</div>
</div>
</div>
</div>
<div class="container d-md-flex py-4">
<div class="me-md-auto text-center text-md-start">
<div class="copyright">
© Copyright <strong><span>IntSaV</span></strong>. All Rights Reserved
</div>
</div>
<div class="social-links text-center text-md-right pt-3 pt-md-0">
<a href="https://twitter.com/intsav_?lang=en-gb" target="_blank" rel="noopener noreferrer" class="twitter"><i class="bx bxl-twitter"></i></a>
<a href="https://www.linkedin.com/company/intelligent-sensing-and-vision-research-group-intsav" target="_blank" rel="noopener noreferrer"><i class="bi bi-linkedin"></i></a>
</div>
</div>
</footer><!-- End Footer -->
<div id="preloader"></div>
<a href="#" class="back-to-top d-flex align-items-center justify-content-center"><i class="bi bi-arrow-up-short"></i></a>
<!-- Vendor JS Files -->
<script src="assets/vendor/aos/aos.js"></script>
<script src="assets/vendor/bootstrap/js/bootstrap.bundle.min.js"></script>
<script src="assets/vendor/glightbox/js/glightbox.min.js"></script>
<script src="assets/vendor/isotope-layout/isotope.pkgd.min.js"></script>
<script src="assets/vendor/php-email-form/validate.js"></script>
<script src="assets/vendor/purecounter/purecounter.js"></script>
<script src="assets/vendor/swiper/swiper-bundle.min.js"></script>
<!-- Template Main JS File -->
<script src="assets/js/main.js"></script>
</body>
</html>