OpenSlide indroduction


OpenSlide is a C library that provides simple interfaces to process Whole-Slide-Images (also known as virtual slides). WSIs are very high resolution images used in digital pathology. These images usually have several gigabytes and can not be easily read with standard tools or libraries because the uncompressed images often occupy tens of gigabytes which consume huge of RAM. WSI is scanned with multi-resolution, while OpenSlide can support read a small amount of image data at the resolution closest to a specifical magnification level.

OpenSlide supports many popular WSI format:

  • Aperio (.svs, .tif)
  • Hamamatsu (.ndpi, .vms, .vmu)
  • Leica (.scn)
  • MIRAX (.mrxs)
  • Philips (.tiff)
  • Sakura (.svslide)
  • Trestle (.tif)
  • Ventana (.bif, .tif)
  • Generic tiled TIFF (.tif)

Although it is written by C, Python and Java bindings are also provided, and some other bindings are contributed in the Github such as Ruby, Rust.

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Learn Prolog

Recently, I got in touch with the Prolog in my coursework (EEEM0005). It is not easy! Prolog is a very old programming language which I only see it in the TIOBE programming language rank (a boring index), but I was attracted by its simple structure and strange programming grammar at begining.

parent(pam, bob).
parent(tom, bob).
parent(tom, liz).
parent(bob, pat).
parent(bob, ann).
parent(pat, jim).

Above all six facts (also called clauses) represent a parent relationship:

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Training Model on Nvidia DIGITS

About Nvidia DIGITS

GPU has high efficient on training model especially for Deep Neural Network. Honestly, training model is not an easy task that we need to prepare datasets, choose network and config lots of parameters. It is a big challenge for you don’t have much experience with programming and frameworks like Python, Py-torch. Nvidia DIGITS is a web platform which allows us train model with user friendly GUI without coding. DIGITS simplifies common deep learning tasks such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real time with advanced visualizations, and selecting the best performing model from the results browser for deployment. DIGITS is completely interactive so that data scientists can focus on designing and training networks rather than programming and debugging.

In this article, I’ll use MNIST dataset and LeNet network to train a model which can classify numbers. Training process can be divided into three steps:

  1. Import Dataset
  2. Training
  3. Testing and analysing

Now we start. If you are confused with how to install DIGITS, please go to the document.

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Salt Pepper噪声以及使用median filter来减少噪声

处理图像时,我们用的图片往往都会有很多噪声。在黑暗中或是设备感光器受到影响,拍出来的图像就会有很多噪声,俗称“噪点”,Salt & Pepper就是其中一种。为什么会叫盐和胡椒粉?因为这些噪声不是白的就是黑的,看起来很像是在图片上撒了盐和胡椒粉,而更专业点的会叫它脉冲噪声,这是在图像信号中突然且尖锐的(sudden and sharp)扰动导致图片变得粗糙。

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上图可知,人类神经系统由三部分构成,Receptors接收到外部环境的刺激后,将这种刺激转换成电脉冲信号传送到Neural net,经过处理,将电脉冲信号发送给Effectors,最后生成可识别的响应。整个环节中有两个方向:

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