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Life beyond the pixels: machine learning and image analysis methods for HCS
Published on Jun 28, 2019104 Views
In this talk I will give an overview of the computational steps in the analysis of a single cell-based high-content screen. First, I will present a novel microscopic image correction method designed t
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Chapter list
Life Beyond the Pixels 00:00
Where…? What…? How many…? - 100:36
Where…? What…? How many…? - 201:08
Where…? What…? How many…? - 301:26
Where…? What…? How many…? - 401:34
Where…? What…? How many…? - 501:43
Where…? What…? How many…? - 601:45
Where…? What…? How many…? - 802:03
How fast can you count? - 102:52
How fast can you count? - 202:55
How fast can you count? - 303:03
How fast can you count? - 403:09
How fast can you count? - 503:17
How fast can you count? - 603:19
How fast can you count? - 703:21
How fast can you count? - 803:22
How fast can you count? - 903:23
How fast can you count? - 1003:24
How fast can you count? - 1103:25
Human vs. Computer03:30
Precise, fast and intelligent04:05
Research activities04:45
Vignetting - 105:42
Vignetting - 205:59
Vignetting - 306:00
Illumination correction - 106:05
Illumination correction - 206:33
Illumination correction - 307:46
Image processing - 108:37
Image processing - 208:53
Image processing - 309:03
Image processing I - 109:10
Image processing I - 209:15
Image processing I - 309:22
Image processing I - 409:24
Image processing II - 109:36
Image processing II - 209:39
Image processing II - 309:49
Image processing II - 409:51
Image processing II - 509:55
Image processing II - 609:58
Image processing III 10:06
Image processing Summary - 110:19
Image processing Summary - 210:20
Challenges10:37
Image segmentation with priors11:05
Single cell tissue analysis12:34
3D extension GPU12:53
3D active surfaces13:15
New active contour model - 113:28
New active contour model - 214:01
Lipid droplet analysis14:43
Size selective droplet analysis - 214:58
Size selective droplet analysis - 115:08
Size selective droplet analysis - 315:30
Size selective droplet analysis - 415:31
Size selective droplet analysis - 515:34
Nuclei in diverse images16:00
Image style transfer learning - 117:06
Image style transfer learning - 217:24
Cell images - 117:42
Cell images - 217:48
Cell images - 317:49
Cell images - 417:51
Cell images - 517:51
Tissue images - 118:09
Tissue images - 218:28
Tissue images - 318:52
Image to image translation 19:18
www.nucleAIzer.org19:30
Cytoplasm segmentation using Mask RCNN - 119:48
Cytoplasm segmentation using Mask RCNN - 220:01
Cytoplasm segmentation using Mask RCNN - 320:03
Cytoplasm segmentation using Mask RCNN - 420:09
Cytoplasm segmentation using Mask RCNN - 520:10
Astrocyte detection20:24
Remember! Image processing. What next? - 122:40
Remember! Image processing. What next? - 222:43
Single cell-based classification for hca22:55
www.cellclassifier.org - 123:06
www.cellclassifier.org - 223:12
Training the machine - 123:25
Training the machine - 223:48
Training the machine - 323:52
Training the machine - 423:53
Automated classification23:58
Future: lets WEB and deeplearn24:15
Classical data processing25:11
Intelligent data processing/mining - 126:05
Intelligent data processing/mining - 226:13
Intelligent data processing/mining - 326:26
Intelligent data processing/mining - 426:49
Two major questions26:54
Active learning for HCS - 127:30
Active learning for HCS - 227:45
Results - 128:26
Phenotype finder30:29
Results - 232:35
Tell me your neighbor...33:27
Major concept - 234:13
Major concept - 135:01
Regression models for HCA - 135:57
Localization of Late Endosome/Lysosomes - 136:23
Localization of Late Endosome/Lysosomes - 236:30
Regression models for HCA - 236:55
Regression models for HCA - 337:20
Regression models for HCA - 437:21
Regression models for HCA - 537:23
Regression plane concept - 137:26
Regression plane concept - 238:10
Further results Semliki Forest virus genome wide screen38:26
Results - 339:30
Lipid droplet phenotype analysis - 140:42
Lipid droplet phenotype analysis - 241:07
Lipid droplet phenotype analysis - 341:25
Lipid droplet phenotype analysis - 441:28
Using supervised classification - 141:36
Using supervised classification - 241:38
Using supervised classification - 341:47
Using supervised classification - 441:48
2D regression plane concept42:14
Example of an annotated plane42:40
Results - 443:12
Results - 543:19
Results - 643:24
Results - 743:29
Results - 843:30
Results - 943:33
Childhood acute lymphoblastic leukemia43:40
Biology idea44:02
Detection of cells44:28
UNET deep learning (prelim) - 144:45
UNET deep learning (prelim) - 244:51
UNET deep learning (prelim) - 344:52
UNET deep learning (prelim) - 445:15
Image based personalized and translational medicine45:35
Personalized precision medicine45:48
Pediatric brain tumors 3D46:55
Advancing 3D single cell based screening47:51
Spheroid generation48:19
Spheroid picker - 148:56
Spheroid picker - 249:30
3D large organoid imaging50:05
Image analysis51:17
Can we learn more? How?51:46
CAMI: Computer Aided Microscopy Isolation - 152:00
CAMI: Computer Aided Microscopy Isolation - 253:32
1. Cerebral cortex54:04
3. Resolving ccRCC heterogeneity (report)55:00
Patient samples55:23
Resolving ccRCC heterogeneity55:26
Definition of morphologically distinct cells in primary ccRCC55:33
AutoPatch : single cell system - 157:54
AutoPatch : single cell system - 201:00:55
Deep learning search01:02:14
Read more about the topic!01:03:06
Join us! European Cell based Assays Interest Group01:03:12
Imaging phenomics01:03:19
Machine learning phenomics01:03:26
3D high content screening01:03:32
Thank you01:03:44