-
Notifications
You must be signed in to change notification settings - Fork 3
/
Copy pathindex.html
342 lines (282 loc) · 17.2 KB
/
index.html
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
---
layout: default
---
<style>
</style>
<div class="ParallaxVideo">
<video autoplay muted loop autbuffer playsinline>
<source src="jays_brain.mp4" type="video/mp4">
<source src="jays_brain.ogg" type="video/ogg">
<source src="jays_brain.webm" type="video/webm">
</video>
<div class="site-info">
<h1>{{ site.name }}</h1>
<p>{{ site.description }}</p>
</div>
</div>
<div class="wideview">
<div class=twitter-section>
<h2>Twitter Feed</h2>
<a class="twitter-timeline" href="https://twitter.com/qtimlab?ref_src=twsrc%5Etfw">Tweets by qtimlab</a> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
</div>
<div class=center>
<div class=home-about>
<h2>HST Virtual Lab Tour</h2>
<p style="text-align:center;">Meet some of the lab and get a feel for some of our projects!</p>
<div class="iframe_container" style="width:40vw; height:50vh; margin:auto;">
<iframe style="width:100%; height:100%; margin:auto; position:relative;"
src="https://www.youtube.com/embed/NcxmYyBYkp0" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
allowfullscreen></iframe>
</div>
<!-- <img src="../images/Full_Lab_Picture.png">
<div class="fun-caption">
<em>Missing: Samarth, Sunakshi, and Alton.</em>
<br />
<br />
</div> -->
<p>Our lab focuses on developing quantitative imaging biomarkers for cancer and other diseases using advanced
imaging techniques and machine learning methods. We are comprised of computer science researchers, medical
physicists, neuro-oncologists, and MRI technicians, and we are always looking to collaborate with experts
outside of our field. We have recently worked to apply deep learning methods to a variety of diseases, and our
goal is to unite the cutting edges of machine learning, medical oncology, and image analysis into practical
clinical applications.</p>
<p>Read about our lab members on the <a href="{{ site.baseurl }}/people">People</a> tab. Learn more about our
specific research topics on the <a href="{{ site.baseurl }}/research">Research</a> tab. See our recent
publications on the <a href="{{ site.baseurl }}/publications">Publications</a> tab, job openings on the <a
href="{{ site.baseurl }}/jobs">Jobs</a> tab, and find a way to get in touch on the <a
href="{{ site.baseurl }}/contact">Contact</a> tab. Last but not least, check the <a
href="{{ site.baseurl }}/fun">Fun</a> tab to see some pictures of our lab members doing what we do best.</p>
</div>
<div class=neural-net>
<ul class="ch-grid">
<li>
<div class="ch-item ch-img-1">
<div class="ch-info">
<div class="ch-info-headline">Brain<br />Lesion<br />Segmentation</div>
<p><a href="research#brainles">Read More...</a></p>
</div>
</div>
</li>
<li class="mobile-hide">
<div class="ch-item ch-img-2">
<div class="ch-info">
<div class="ch-info-headline">Deep<br />Radio<br />Genomics</div>
<p><a href="research#idh">Read More...</a></p>
</div>
</div>
</li>
<li>
<div class="ch-item ch-img-3">
<div class="ch-info">
<div class="ch-info-headline"><br />Retinopathy of Prematurity</div>
<p><a href="research#rop">Read More...</a></p>
</div>
</div>
</li>
</ul>
<div class="weights">
<svg class="small-hide" height="100%" width="100%">
<line class="weight-line mobile-hide" x1="16%" y1="0%" x2="34%" y2="100%" stroke-width="2px" stroke="black" />
<line class="weight-line mobile-hide" x1="16%" y1="0%" x2="66%" y2="100%" stroke-width="2px" stroke="black" />
<line class="weight-line mobile-hide" x1="34%" y1="100%" x2="50%" y2="0%" stroke-width="2px" stroke="black" />
<line class="weight-line mobile-hide" x1="50%" y1="0%" x2="66%" y2="100%" stroke-width="2px" stroke="black" />
<line class="weight-line mobile-hide" x1="34%" y1="100%" x2="84%" y2="0%" stroke-width="2px" stroke="black" />
<line class="weight-line mobile-hide" x1="66%" y1="100%" x2="84%" y2="0%" stroke-width="2px" stroke="black" />
<line class="weight-line mobile-only" x1="23%" y1="0%" x2="50%" y2="100%" stroke-width="2px" stroke="black" />
<line class="weight-line mobile-only" x1="50%" y1="100%" x2="77%" y2="0%" stroke-width="2px" stroke="black" />
</svg>
</div>
<ul class="ch-grid">
<li>
<div class="ch-item ch-img-4">
<div class="ch-info">
<div class="ch-info-headline">Open <br> Source <br> Software</div>
<p><a href="research#software">Read More...</a></p>
</div>
</div>
</li>
<li class="mobile-hide">
<div class="ch-item ch-img-5">
<div class="ch-info">
<div class="ch-info-headline">Distributed <br> Deep <br> Learning</div>
<p><a href="research#distributed">Read More...</a></p>
</div>
</div>
</li>
</ul>
<div class="weights">
<svg class="small-hide" height="100%" width="100%">
<line class="weight-line mobile-hide" x1="16%" y1="100%" x2="34%" y2="0%" stroke-width="2px" stroke="black" />
<line class="weight-line mobile-hide" x1="16%" y1="100%" x2="66%" y2="0%" stroke-width="2px" stroke="black" />
<line class="weight-line mobile-hide" x1="34%" y1="0%" x2="50%" y2="100%" stroke-width="2px" stroke="black" />
<line class="weight-line mobile-hide" x1="50%" y1="100%" x2="66%" y2="0%" stroke-width="2px" stroke="black" />
<line class="weight-line mobile-hide" x1="34%" y1="0%" x2="84%" y2="100%" stroke-width="2px" stroke="black" />
<line class="weight-line mobile-hide" x1="66%" y1="0%" x2="84%" y2="100%" stroke-width="2px" stroke="black" />
<line class="weight-line mobile-only" x1="50%" y1="0%" x2="23%" y2="100%" stroke-width="2px" stroke="black" />
<line class="weight-line mobile-only" x1="50%" y1="0%" x2="77%" y2="100%" stroke-width="2px" stroke="black" />
</svg>
</div>
<ul class="ch-grid">
<li>
<div class="ch-item ch-img-6">
<div class="ch-info">
<div class="ch-info-headline">Oncology <br> Clinical <br> Trials</div>
<p><a href="research#clinical">Read More...</a></p>
</div>
</div>
</li>
<li>
<div class="ch-item ch-img-7">
<div class="ch-info">
<div class="ch-info-headline">Dynamic<br>MRI<br>Analysis</div>
<p><a href="research#dce">Read More...</a></p>
</div>
</div>
</li>
<li class="mobile-hide">
<div class="ch-item ch-img-8">
<div class="ch-info">
<div class="ch-info-headline">Visual <br> Machine <br> Learning</div>
<p><a href="research#viz">Read More...</a></p>
</div>
</div>
</li>
</ul>
</div>
</div>
<div class=news>
<h2>News</h2>
<ul>
<li><span class=date>01/26/2023</span> - Katharina Höebel defends her thesis. The title: Domain and User-centered Machine Learning for Medical Image Analysis.</li>
<li><span class=date>06/10/2022</span> - QTIM opens branch at the University of Colorado Denver | Anschutz Medical Campus!</li>
<li><span class=date>02/01/2022</span> - Stay tuned for another of Kathi Hoebel's presentations: <a
href="https://spie.org/medical-imaging/presentation/Is-this-good-enough-On-expert-perception-of-brain-tumor/12035-29?enableBackToBrowse=true&SSO=1">Is
this good enough? On expert perception of brain tumor segmentation quality</a>, which will be published in
SPIE 2022 and is part of the <a href="https://spie.org/conferences-and-exhibitions/medical-imaging">SPIE Medical
Imaging Conference</a>! This is paper 12035-29.</li>
<li><span class=date>02/01/2022</span> - Stay tuned for Kathi Hoebel's presentation: <a
href="https://spie.org/medical-imaging/presentation/Do-I-know-this-Segmentation-uncertainty-under-domain-shift/12032-27?enableBackToBrowse=true">Do
I know this? Segmentation uncertainty under domain shift</a>, which will be published in SPIE 2022 and is part
of the <a href="https://spie.org/conferences-and-exhibitions/medical-imaging">SPIE Medical Imaging
Conference</a>! This is paper 12032-27.</li>
<li><span class=date>12/1/2021</span> - Shruti Raghavan's RSNA Abstract <i>Automatic Detection Of Adrenal Nodules
In
Radiology Reports: Proof Of Concept For Using Recurrent Neural Networks For Cohort Creating Using Radiology
reports</i> was accepted and will be at 9:30am presented by Dr. Jayashree Kalpathy-Cramer.</li>
<li><span class=date>11/29/2021</span> - Mishka Gidwani's RSNA Abstract <i>Radiomic P-hacking: Corrupting The
Training/validation/test/Split</i> was accepted and will be at 12:45pm.</li>
<li><span class=date>12/01/2021</span> - Jay Patel's RSNA Abstract <i>Fully Automatic Segmentation And Treatment
Response Assessment Of Brain Metastases On Magnetic Resonance Imaging</i> was accepted and will be at
1:30-2:30pm.
</li>
<li><span class=date>12/01/2021</span> - Chris Bridge's RSNA Abstract <i>A Fully Automated Pipeline For
Multi‐vertebral
Level Quantification And Characterization Of Muscle And Adipose Tissue On Chest Computed Tomography</i> was
accepted
and will be at 3pm.</li>
<li><span class=date>11/30/2021</span> - Charlie Lu's RSNA Abstract <i>Subgroup Analysis Highlights Fairness
Challenges
For Deep Learning Algorithms In Breast Density Estimation</i> was accepted and he presents at 12:15 - 12:45pm.
</li>
<li><span class=date>10/06/2021</span> - QTIM releases <a
href="https://pubs.rsna.org/doi/10.1148/ryai.2020190199">Assessing the (Un)Trustworthiness of Saliency Maps
for
Localizing Abnormalities in Medical Imaging</a>! All eight saliency map techniques fail at least one of the
criteria
and were inferior in performance compared with localization networks.</li>
<li><span class=date>12/16/2020</span> - QTIM releases <a
href="https://pubs.rsna.org/doi/10.1148/ryai.2020190199">Radiomics Repeatability Pitfalls in a Scan-Rescan MRI
Study of Glioblastoma</a>! This study uses a siamese neural network-based severity score that automatically
measures radiographic COVID-19 pulmonary disease severity, which can be used to track disease change and predict
subsequent intubation or death.</li>
<li><span class=date>11/15/2020</span> - QTIM releases <a href="https://arxiv.org/abs/2011.07482">Towards
Trainable
Saliency Maps in Medical Imaging</a>! Our results have implications for the clinical deployment of deep
learning
models, increasing
their utility and explainability for clinicians.</li>
<li><span class=date>7/22/2020</span> - QTIM releases <a
href="https://pubs.rsna.org/doi/10.1148/ryai.2020200079">Automated Assessment and Tracking of COVID-19
Pulmonary Disease Severity on Chest Radiographs using Convolutional Siamese Neural Networks</a>! We developed
an automated Siamese neural network-based pulmonary disease severity score for patients with COVID-19, with the
potential to help with clinical triage and workflow optimization.</li>
<li><span class=date>6/24/2020</span> - QTIM releases breast density classification algorithm in <a
href="https://www.jacr.org/article/S1546-1440(20)30539-1/fulltext">JACR</a>! This article demonstrates the
possible parameters that can influence the performance of the model and how crowdsourcing can be used for
evaluation.</li>
<li><span class=date>6/23/2020</span> - <a
href="https://link.springer.com/article/10.1007/s12021-020-09477-5">DeepNeuro: An Open-Source Deep Learning
Toolbox for Neuroimaging</a> was published in <a href="https://pubmed.ncbi.nlm.nih.gov/32578020/">PebMed</a>.
DeepNeuro is a Python-based deep learning framework that puts deep neural networks for neuroimaging into
practical usage with a minimum of friction during implementation.</li>
<li><span class=date>5/15/2020</span> - QTIM's paper <a
href="https://openreview.net/forum?id=02X3kfP6W4">Assessing the validity of saliency maps for abnormality
localization in medical imaging</a> was published as part of <a
href="https://2020.midl.io/program/short-papers.html">MIDL Montréal 2020</a>, a conference which brings deep
learning and medical imaging researchers together for in-depth discussion and exchange of ideas.</li>
<li><span class=date>3/10/2020</span> - Katharina Hoebel's paper <a
href="https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11313/113131K/An-exploration-of-uncertainty-information-for-segmentation-quality-assessment/10.1117/12.2548722.short?SSO=1">An
exploration of uncertainty information for segmentation quality assessment</a> was published as part of <a
href="https://spie.org/conferences-and-exhibitions/medical-imaging/proceedings?SSO=1">SPIE medical imaging
proceedings.</a></li>
<li><span class=date>3/6/2020</span> - <a href="">Siamese neural networks for evaluation of disease severity and
change on a continuous spectrum in medical imaging</a> accepted to <a
href="https://www.nature.com/npjdigitalmed/about/aims">npj Digital Medicine</a></li>
<li><span class=date>11/13/2019</span> - Sean Ko's abstract <a
href="https://ashpublications.org/blood/article-abstract/134/Supplement_1/2156/427904/Machine-Learning-Based-Predictive-Model-of-5-Year?redirectedFrom=fulltext">Machine
Learning Based Predictive Model of 5-Year Survival in Multiple Myeloma Autologous Transplant Patients</a> is
published in <a href="https://www.hematology.org/">The American Society of Hematology</a></li>
<li><span class=date>10/18/2019</span> - Post Doc <a href="https://www.linkedin.com/in/ikbeom-jang/">Ikbeom
Jang</a> joined the lab</li>
<li><span class=date>10/18/2019</span> - Masters student <a href="https://www.linkedin.com/in/bryanchen96/">Bryan
Chen</a> joined the lab</li>
<li><span class=date>10/17/2019</span> - <i>Newly Published Literature: </i>QTIM paper <a href="">An exploration
of uncertainty information for segmentation quality assessment</a> has been accepted for <strong>oral
presentation</strong> at <a href="https://spie.org/conferences-and-exhibitions/medical-imaging?SSO=1">SPIE
Medical Imaging 2020!</a></li>
<li><span class=date>10/07/2019</span> - Masters student <a href="https://www.linkedin.com/in/sean-ko/">Sean
Ko</a> joined the lab</li>
<li><span class=date>10/01/2019</span> - <a
href="https://scholar.google.com/citations?user=UpGsAykAAAAJ&hl=en">Katharina Höbel's</a> extended abstract <a
href="https://arxiv.org/abs/1911.06357">Give me (un)certainty - An exploration of parameters that affect
segmentation uncertainty</a> was accepted to <a href="https://ml4health.github.io/2019/">ML4H</a> at <a
href="https://www.nips.cc/">NeurIPS 2019</a></li>
<li><span class=date>9/26/2019</span> - <i>Newly Published Literature: </i> <a
href="https://www.ncbi.nlm.nih.gov/pubmed/31558474">Bevacizumab reduces permeability and concurrent
temozolomide delivery in a subset of patients with recurrent glioblastoma.</a></li>
<li><span class=date>7/15/2019</span> - Data Scientist <a href="https://www.linkedin.com/in/ikbeom-jang/">Ikbeom
Jang</a> joined the lab</li>
<li><span class=date>7/9/2019</span> - <i>Newly Published Literature: </i> <a
href="https://www.ncbi.nlm.nih.gov/pubmed/31295616">Machine Learning Models can Detect Aneurysm Rupture and
Identify Clinical Features Associated with Rupture.</a></li>
<li><span class=date>7/2019</span> - <i>Newly Published Literature: </i> <a
href="https://www.ncbi.nlm.nih.gov/pubmed/31272590">Democratizing AI.</a></li>
<li><span class=date>6/13/2019</span> - Postdoc <a
href="https://scholar.google.com/citations?user=12RkAfQAAAAJ&hl=en">Praveer Singh</a> joined the lab</li>
</ul>
</div>
</div>
<!--
<div class=new_section>
<h1>One</h1>
</div>
<div class=new_section>
<h1>Two</h1>
</div>
<div class=new_section>
<h1>Three</h1>
</div>
-->
<!-- <div class="posts">
{% for post in site.posts %}
<article class="post">
<h1><a href="{{ site.baseurl }}{{ post.url }}">{{ post.title }}</a></h1>
<div class="entry">
{{ post.excerpt }}
</div>
<a href="{{ site.baseurl }}{{ post.url }}" class="read-more">Read More</a>
</article>
{% endfor %}
</div>
-->