Deep learning is showing a growing trend in data analysis and is known as one of the 10 breakthrough technologies in 2013. It is an improvement over neural networks that includes more layers of computation to enable higher levels of abstraction and prediction in the data. So far, it is becoming the leading machine learning tool in the field of general imaging and computer vision.
![[Depth] Analyze how deep learning changes the field of medical imaging](http://i.bosscdn.com/blog/17%20(1).png)
In particular, Convolutional Neural Networks (CNN) have proven to be an advantageous tool for many computer vision tasks. Deep CNN automatically learns intermediate and advanced abstractions derived from raw data (eg, images). Recent results show that the generic descriptor extracted from CNN is very effective in object recognition and localization of natural images. Medical image analysis groups around the world are rapidly entering the field and applying CNN and other deep learning methods to a wide range of applications. Many good results are emerging.
In the field of medical imaging, accurate diagnosis or assessment of disease depends on image acquisition and image interpretation. In recent years, with the development of technology, devices can collect data at a faster rate and more powerful resolution, which greatly improves the quality of image acquisition. However, improvements in image interpretation by computer technology are just beginning. Currently, most medical image interpretations are performed by doctors. However, image interpretation by humans is often one-sided because of its subjectivity, large changes in different interpreters, and fatigue. Many diagnostic tasks require an initial search process to detect anomalies and quantify changes in measurements and time. Computerized tools, especially image analysis and machine learning, play a key role in improving diagnosis. They support expert workflows by helping identify areas that require treatment. Among these tools, deep learning is quickly confirmed as a basis for superiority and accuracy. It has also opened up new areas of data analysis and is evolving at an unprecedented rate.
A. Historical network
The basic ideas behind neural networks and deep learning have existed for decades. They usually have only a few layers. The emergence of backpropagation algorithms has led to a significant increase in the performance of neural networks. However, performance is still not enough. Other classifiers have evolved, including decision trees, boosting, and support vector machines. Each of them has been applied to medical image analysis, especially for detecting anomalies, and they have also been applied in other related fields such as segmentation. Despite this development, relatively high false positive rates are still common.
As early as 1996 in the work of Sahiner et al., CNN (Convolutional Neural Network) was applied to medical image processing. In this work, ROIs (Region of Interests) containing biopsy-confirmed masses or normal tissues were extracted from mammograms. The CNN contains an input layer, two hidden layers, and an output layer, as well as backpropagation used. In this pre-GPU era, training time was described as "calculatively intensive" but did not give specific time. In 1993, CNN was used for lung nodule testing. In 1995, CNN was used to detect microcalcifications on mammograms.
A typical CNN for image processing consists of a series of layers of convolution filters interspersed with a series of data compression or pooling layers. A convolution filter processes a small block of the input image. Similar to low-level pixel processing of the human brain, convolution filters can detect highly correlated image features, such as lines or circles that can represent sharp edges (for example, for organ detection) or circles (such as objects for circles, Like colon polyps, then high-order features such as local or global shapes and textures. The output of a CNN is typically a label of one or more probabilities or categories corresponding to the image. The convolution filter can learn directly from the trained data. This is exactly what people need because it reduces the need for time-tagged features that take time. If a convolution filter is not used, then in the pre-processed image phase, filters designed for a particular application and some features that need to be computed are inseparable from these artifacts.
CNN is a highly parallelized algorithm. Much of the utility of using CNN is due to the huge speed increase (approximately 40 times) that is facilitated by the image processing unit (GPU) compared to separate CPU processing. An early paper describing the value of GPUs for training CNN and other machine learning techniques was published in 2006. In medical image processing, GPUs are first introduced for segmentation, reconstruction, and registration, and then machine learning. Interestingly, although Eklund et al. talked extensively about convolution in their 2013 paper, convolutional neural networks and deep learning were not mentioned at all. This highlights how rapidly the major reforms in deep learning have rapidly adjusted medical image processing research.
Black tea (dark tea) is named after the black appearance of the finished tea. It is one of the six major tea groups and is a post-fermented tea. The main production areas are Guangxi, Sichuan, Yunnan, Hubei, Hunan, Shaanxi and Anhui. The traditional black tea is made from high maturity black wool tea, which is the main raw material for pressing tightly pressed tea.
The process of making black tea generally includes four steps: killing, kneading, stacking and drying. Black tea is mainly classified as Hunan black tea (Fu tea, Qianliang tea, black brick tea, Tri-tip, etc.), Hubei green brick tea, Sichuan Tibetan tea (border tea), Anhui Gu Yi black tea (An tea), Yunnan black tea (Pu'er tea), Guangxi Liubao tea and Shaanxi black tea (Fu tea) according to geographical distribution.
The yellow powder in black tea is commonly known as golden flower, a beneficial microorganism, the so-called golden flower, is in the processing of raw materials through the flowering of this special procedure, specifically in the black tea bricks to cultivate a kind of coronary aspergillus substance called coronary scattered cystic bacteria, commonly known as the golden flower, observed under the microscope, each individual golden flower is umbrella-shaped clusters, the whole body is golden and eye-catching, ringed into a group, evenly distributed.
black tea powder; dark tea powder
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