Machine learning / Computer science: Analyzing human induced pluripotent stem cell images with deep neural networks
Organisatsiooni nimi
ATI
Kokkuvõte
To start understanding how individual cells work, their characteristics have to be measured. A standard way to do this is using high throughput microscopy, which is a rich source of quantitative data, with thousands of pixel values measured for every cell. The main problem in making use of this large scale information is extracting meaningful features. There are many existing software packages that are able to generate numbers from each cell image, ranging from area and shape characteristics to values for Zernike features and Gabor filters, but these hundreds of numbers rarely correspond directly to a biologically interpretable signal. Alternatively, a desired biological characteristic can be modelled and quantified from the images, but this requires extensive feature engineering.
A promising alternative to the abundant local and scarce broad global image features is to extract them from deep neural networks. These models are able to make use of large-scale data to learn spatial correlations that are not easily inferred from standard parametric models. In the last years, this class of methods has outperformed alternatives on most image processing tasks, ranging from object detection to semantic embedding.
The aim of this project is to train a multilayer convolutional neural network to represent the microscopy images in a low-dimensional space, and to assess whether the neuron activities correspond to meaningful signal. This is the case for yeast cells [1], and we are now ready to test whether it holds for human cells as well.
The Wellcome Trust has funded an initiative (www.hipsci.org) to create induced pluripotent stem cell lines from hundreds of healthy and diseased individuals. These cells are special, as they have been reverted to a ground (“pluripotent”) state, in which they can be propagated in the lab, and from which they can be differentiated into many different cell types. Many cell images have been acquired from each donor, and are publicly available, together with a wealth of genomic data. In addition, we have been awarded a grant from NVIDIA, which provided us with a new Tesla GPU hosted at the Department of Computer Science in Tartu.
This computational project is suitable for someone with relatively good computer engineering and hacking skills, as well as some background in mathematics.
References
Tanel Pärnamaa, Leopold Parts. “Accurate classification of protein subcellular localization from high throughput microscopy images using deep learning”. doi: http://dx.doi.org/10.1101/050757
"Eesti teadlased õpetasid arvuti ülitäpselt rakupiltidest aru saama" Novaator, 2016. http://novaator.err.ee/v/tehnika/783488f3-f41b-4961-af8b-9411be2ce9e8/eesti-teadlased-opetasid-arvuti-ulitapselt-rakupiltidest-aru-saama
A promising alternative to the abundant local and scarce broad global image features is to extract them from deep neural networks. These models are able to make use of large-scale data to learn spatial correlations that are not easily inferred from standard parametric models. In the last years, this class of methods has outperformed alternatives on most image processing tasks, ranging from object detection to semantic embedding.
The aim of this project is to train a multilayer convolutional neural network to represent the microscopy images in a low-dimensional space, and to assess whether the neuron activities correspond to meaningful signal. This is the case for yeast cells [1], and we are now ready to test whether it holds for human cells as well.
The Wellcome Trust has funded an initiative (www.hipsci.org) to create induced pluripotent stem cell lines from hundreds of healthy and diseased individuals. These cells are special, as they have been reverted to a ground (“pluripotent”) state, in which they can be propagated in the lab, and from which they can be differentiated into many different cell types. Many cell images have been acquired from each donor, and are publicly available, together with a wealth of genomic data. In addition, we have been awarded a grant from NVIDIA, which provided us with a new Tesla GPU hosted at the Department of Computer Science in Tartu.
This computational project is suitable for someone with relatively good computer engineering and hacking skills, as well as some background in mathematics.
References
Tanel Pärnamaa, Leopold Parts. “Accurate classification of protein subcellular localization from high throughput microscopy images using deep learning”. doi: http://dx.doi.org/10.1101/050757
"Eesti teadlased õpetasid arvuti ülitäpselt rakupiltidest aru saama" Novaator, 2016. http://novaator.err.ee/v/tehnika/783488f3-f41b-4961-af8b-9411be2ce9e8/eesti-teadlased-opetasid-arvuti-ulitapselt-rakupiltidest-aru-saama
Lõputöö kaitsmise aasta
2016-2017
Juhendaja
Leopold Parts
Suhtlemiskeel(ed)
eesti keel, inglise keel
Nõuded kandideerijale
This computational project is suitable for someone with relatively good computer engineering and hacking skills, as well as some background in mathematics.
Tase
Bakalaureus, Magister
Märksõnad
Kandideerimise kontakt
Nimi
Leopold Parts
Tel
E-mail