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Deep Learning: Opening Doors to New Applications

Join Manoel and Kathy as they explain how to unleash the power of deep learning to tackle challenging image analysis tasks, such as detecting cells in brightfield images and cell classification tasks that are difficult for the human eye.

Presenters:

Manoel Veiga, Application Specialist, Life Science Research
Kathy Lindsley, Application Specialist, Life Science

FAQ

Webinar FAQs | Deep Learning

Can you do quantitative intensity measurements after deep learning algorithms are applied?

The deep learning algorithms predict the position of the fluorescent signal but not the intensity. However, you can perform segmentation based on deep learning and then use the secondary channel to perform a quantitative intensity analysis (e.g., fluorescence).

For example, if you’re going label-free and want to measure a protein expression and irradiate your specimen with very low power, then you can perform the segmentation in the brightfield image, then measure the fluorescence in the secondary channel. And if your intensity is low but has a constant background, you can perform a quantitative analysis—even if the signal is just a couple of counts above the camera noise.

Can deep learning software be applied to stained histology slides (e.g., H&E)?

Yes, there is a special neural network architecture in cellSens™ software for RGB images. This RGB network has an augmentation procedure that slightly modifies the contributions of the different colors, ensuring the neural network is robust to slight variations in RGB and the balance of colors.

How many images does deep learning software need for training?

The key parameter is the number of objects annotated rather than the number of images. In some cases, 20 to 30 objects would work, but then you can only use that neural network to analyze images with similar contrast. If you want to go beyond that to go label-free and analyze objects in difficult conditions, then you will typically need thousands of annotations. This high level of annotations can be achieved by applying an automated ground truth using fluorescence, for example.

Are Olympus deep learning algorithms based on U-Net?

Yes, they are inspired by U-Net. They are not exactly the same, but the overall structure is based on U-Net.

What is the difference between deep neural networks and convolutional neural networks?

Neural networks have an input and an output layer. A deep neural network has at least one intermediate layer between the input and the output layers (generally they have several intermediate layers). A convolutional neural network is a class of deep neural networks where the intermediate layers are convolved with each other. Convolution is a mathematical operation that works very well for imaging analysis tasks. For this reason, convolutional neural networks are used to analyze microscopy images. Deep learning is also used in other fields outside of image analysis. These applications do not require convolutional networks and use other types of networks instead.


相关产品

成像软件

cellSens

  • 模块化成像软件平台
  • 直观的应用驱动型用户界面
  • 从简单快照到高级多维实时实验的各种功能组合

Deep Learning: Opening Doors to New Applications

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Webinar: Deep Learning: Opening Doors to New Applications

Join Manoel and Kathy as they explain how to unleash the power of deep learning to tackle challenging image analysis tasks, such as detecting cells in brightfield images and cell classification tasks that are difficult for the human eye.

FAQ

Webinar FAQs | Deep Learning

Can you do quantitative intensity measurements after deep learning algorithms are applied?

The deep learning algorithms predict the position of the fluorescent signal but not the intensity. However, you can perform segmentation based on deep learning and then use the secondary channel to perform a quantitative intensity analysis (e.g., fluorescence).

For example, if you’re going label-free and want to measure a protein expression and irradiate your specimen with very low power, then you can perform the segmentation in the brightfield image, then measure the fluorescence in the secondary channel. And if your intensity is low but has a constant background, you can perform a quantitative analysis—even if the signal is just a couple of counts above the camera noise.

Can deep learning software be applied to stained histology slides (e.g., H&E)?

Yes, there is a special neural network architecture in cellSens™ software for RGB images. This RGB network has an augmentation procedure that slightly modifies the contributions of the different colors, ensuring the neural network is robust to slight variations in RGB and the balance of colors.

How many images does deep learning software need for training?

The key parameter is the number of objects annotated rather than the number of images. In some cases, 20 to 30 objects would work, but then you can only use that neural network to analyze images with similar contrast. If you want to go beyond that to go label-free and analyze objects in difficult conditions, then you will typically need thousands of annotations. This high level of annotations can be achieved by applying an automated ground truth using fluorescence, for example.

Are Olympus deep learning algorithms based on U-Net?

Yes, they are inspired by U-Net. They are not exactly the same, but the overall structure is based on U-Net.

What is the difference between deep neural networks and convolutional neural networks?

Neural networks have an input and an output layer. A deep neural network has at least one intermediate layer between the input and the output layers (generally they have several intermediate layers). A convolutional neural network is a class of deep neural networks where the intermediate layers are convolved with each other. Convolution is a mathematical operation that works very well for imaging analysis tasks. For this reason, convolutional neural networks are used to analyze microscopy images. Deep learning is also used in other fields outside of image analysis. These applications do not require convolutional networks and use other types of networks instead.


相关产品

成像软件

cellSens

  • 模块化成像软件平台
  • 直观的应用驱动型用户界面
  • 从简单快照到高级多维实时实验的各种功能组合
Experts
Kathy Lindsley
生命科学应用应用专家

我是Kathy Lindsley,是奥林巴斯为基于相机成像系统提供支持的应用专家。我拥有爱荷华州立大学生物化学专业的理学学士学位。我于2006年加入奥林巴斯,担任研究成像销售代表,并于2012年加入生命科学应用团队。在加入奥林巴斯之前,我曾在学术研究领域担任过15年的研究助理,在膜片钳、钙成像、组织培养和免疫组织化学方面积累了丰富的经验。

Manoel Veiga
生命科学研究应用专家

大家好。我叫Manoel Veiga,是奥林巴斯软件深度学习实施团队成员。我于2017年加入奥林巴斯,拥有高内涵筛选、图像分析和深度学习领域的专业知识。我还是荧光寿命成像的专家。

在攻读物理化学博士学位期间,我开始对数据分析产生兴趣,在了解到卷积神经网络的强大功能及其所能完成难以置信的图像分析任务之后,更加加深了我对这一领域的兴趣。

Deep Learning: Opening Doors to New Applications2021年10月17日
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