CN110288542A - A kind of liver's pathological image sample Enhancement Method based on stochastic transformation - Google Patents
A kind of liver's pathological image sample Enhancement Method based on stochastic transformation Download PDFInfo
- Publication number
- CN110288542A CN110288542A CN201910526943.0A CN201910526943A CN110288542A CN 110288542 A CN110288542 A CN 110288542A CN 201910526943 A CN201910526943 A CN 201910526943A CN 110288542 A CN110288542 A CN 110288542A
- Authority
- CN
- China
- Prior art keywords
- image
- liver
- sample
- pathological image
- pathological
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000001575 pathological effect Effects 0.000 title claims abstract description 47
- 210000004185 liver Anatomy 0.000 title claims abstract description 35
- 230000009466 transformation Effects 0.000 title claims abstract description 29
- 238000000034 method Methods 0.000 title claims abstract description 26
- 238000013136 deep learning model Methods 0.000 claims abstract description 12
- 230000002708 enhancing effect Effects 0.000 claims abstract description 6
- 230000008859 change Effects 0.000 claims description 4
- 238000006243 chemical reaction Methods 0.000 claims description 4
- 230000007812 deficiency Effects 0.000 abstract description 6
- 238000004458 analytical method Methods 0.000 abstract description 5
- 230000007170 pathology Effects 0.000 abstract description 4
- 238000012549 training Methods 0.000 description 9
- 201000011510 cancer Diseases 0.000 description 6
- 238000003745 diagnosis Methods 0.000 description 6
- 206010028980 Neoplasm Diseases 0.000 description 4
- 201000007270 liver cancer Diseases 0.000 description 4
- 208000014018 liver neoplasm Diseases 0.000 description 4
- 206010073069 Hepatic cancer Diseases 0.000 description 3
- 201000010099 disease Diseases 0.000 description 3
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 3
- 206010073071 hepatocellular carcinoma Diseases 0.000 description 3
- 201000009030 Carcinoma Diseases 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000001574 biopsy Methods 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000003759 clinical diagnosis Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 230000002950 deficient Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000009792 diffusion process Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000002440 hepatic effect Effects 0.000 description 1
- 238000007489 histopathology method Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 230000003902 lesion Effects 0.000 description 1
- 230000001394 metastastic effect Effects 0.000 description 1
- 206010061289 metastatic neoplasm Diseases 0.000 description 1
- 238000010827 pathological analysis Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000000452 restraining effect Effects 0.000 description 1
- 230000001225 therapeutic effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformations in the plane of the image
- G06T3/60—Rotation of whole images or parts thereof
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/77—Retouching; Inpainting; Scratch removal
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20021—Dividing image into blocks, subimages or windows
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30056—Liver; Hepatic
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Radiology & Medical Imaging (AREA)
- Quality & Reliability (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Investigating Or Analysing Biological Materials (AREA)
Abstract
Liver's pathological image sample Enhancement Method based on stochastic transformation that the present invention relates to a kind of comprising following steps: 1) block division is carried out to liver pathological image, obtains several image fritters；2) stochastic transformation is carried out to each image fritter, forms exptended sample；The stochastic transformation includes one or more of horizontal mirror image switch, vertical mirror overturning, cutting, brightness adjustment, saturation degree adjustment and adjustment of color；3) exptended sample input deep learning model is trained liver's pathological image, and carries out corresponding enhancing with liver pathological image, obtain the enhancing sample of liver's pathological image.The present invention can effectively expand original pathology sample, solve the problems, such as that sample size deficiency and sample distribution are uneven to a certain extent, meet the requirement of deep learning model large sample size, the model trained can be effectively avoided over-fitting occur, the situation of generalization ability deficiency improves the reliability of assistant analysis result.
Description
Technical field
The present invention relates to digital pathological image processing technology field more particularly to a kind of liver's tissues based on OTSU threshold value
Pathological image dividing method.
Background technique
Liver cancer (liver cancer) refers to the malignant tumour for betiding liver, including primary carcinoma of liver and metastatic hepatic carcinoma
Two kinds, the liver cancer of the daily theory of people refer to it is mostly be primary carcinoma of liver.Primary carcinoma of liver be clinically the most common malignant tumour it
One, according to recent statistics, the whole world newly sends out liver cancer patient about 600,000 every year, occupies the 5th of malignant tumour.For different
Cancer cell-types, cancer cell diffusion have different effective therapeutic modalities.Currently, being especially most of cancers in clinical diagnosis
In disease diagnosis, by biopsy, fall off with Fine-needle Aspiration Cytology etc., to the living tissue of patient's lesion region
Be sliced the pathological examination results being observed and analyzed under microscopic view and obtained be doctor carry out medical diagnosis on disease it is important according to
It is considered as " goldstandard " of cancer diagnosis according to even final foundation, the diagnosis based on pathological images.
Currently, the diagnosis with pathological picture is mainly manually checked by the doctor of profession, with the day of number of patients
Benefit increases and to the continuous improvement that medical diagnosis on disease accuracy rate requires, and the pathological images quantity for needing to analyze is doubled and redoubled, therefore needs
Increase more personnel, equipment copes with a greater amount of histopathological analysis demands.However the national conditions according to China at this stage,
Experienced doctor's rare numbers, and it is horizontal irregular, pathology department's room level of digital is generally relatively low, and digital pathology equipment is tight
Weight is deficient, this brings great difficulty to the further development of pathological diagnosis.In view of storing the convenience with remote transmission,
Digitlization histopathology image is increasingly taken seriously, it is often more important that, digitlization histopathology image is to introduce intelligence auxiliary
Analysis provides possibility to mitigate doctor's burden, significant to the nervous situation for alleviating medical resource.
Instantly the intelligent assistant analysis means of mainstream include: image characteristics extraction, deep learning model etc., and these are auxiliary
Analysis method is helped to generally require a large amount of data sample.It is to have training samples more as far as possible first by taking deep learning as an example,
The secondary distribution for being to ensure that sample is enough uniform, but actual conditions are possible to be frequently encountered the inadequate situation of sample size, cannot
Meet the training requirement of deep learning model.
Summary of the invention
It is an object of the invention in view of the shortcomings of the prior art, provide it is a kind of design rationally, be able to solve pathological section figure
Excessive and sample size deficiency problem liver's pathological image sample Enhancement Method based on stochastic transformation.
To achieve the above object, the invention adopts the following technical scheme:
A kind of liver's pathological image sample Enhancement Method based on stochastic transformation comprising following steps:
1) block division is carried out to liver's pathological image, obtains several image fritters；
2) stochastic transformation is carried out to each image fritter, forms exptended sample；The stochastic transformation include horizontal mirror image switch,
One or more of vertical mirror overturning, cutting, brightness adjustment, saturation degree adjustment and adjustment of color；
3) exptended sample input deep learning model is trained liver's pathological image, and is carried out pair with liver pathological image
It should enhance, obtain the enhancing sample of liver's pathological image.
Preferably, the size of image fritter described in step 1) is 320*320 pixel.
Preferably, the method for horizontal mirror image switch described in step 2) and vertical mirror overturning are as follows:
If the width of image fritter is width, length height, the former coordinate in image fritter before a certain point transformation is (x0,
y0), transformed coordinate is (x, y), then,
Horizontal mirror image switch needs to meet:
X=width-x0-1
Y=y0；
Vertical mirror overturning, needs to meet:
Preferably, the method for cutting described in step 2) are as follows: keep the resolution ratio of image fritter constant, if image fritter
Width be width, length height, the cutting coefficient of width and length is respectively a and b, then, the image after cutting is wide
Degree is width*a, length height*b.
Preferably, the method for brightness adjustment described in step 2), saturation degree adjustment and adjustment of color are as follows:
HSI is converted by image fritter rgb pixel value, conversion formula is as follows:
Wherein,
Saturation degree component is given by:
Strength component is given by:
The change coefficient of HSI three is both configured to 0.1, the HSI of image fritter is changed into original 0.9 or 1.1 at random
Times, complete the adjustment to the small Block Brightness of image, saturation degree or tone.
To guarantee training up and effectively restraining for deep learning model, needs to acquire a large amount of samples pictures before training and make
Input when for model training, the pathological section figure that doctor can provide under normal conditions marked it is extremely limited, often
It is not able to satisfy the training requirement of deep learning model.
The present invention is built upon using pathological section figure under the background for training deep learning model, and there are two for the background
A problem makes pathological section figure be difficult to be used directly to training pattern: first is that picture is too big, can not be used directly to training pattern, need
It is cut into fritter；Second is that the quantity of slice map is few, so the sample size of directly piecemeal formation is also few, the instruction of model will lead to
White silk is insufficient.
The invention adopts the above technical scheme, by carrying out block division to liver's pathological image, obtains several images
Fritter, and stochastic transformation is carried out to each image fritter, exptended sample is formed, so as to effectively expand original pathology
Sample solves the problems, such as that sample size deficiency and sample distribution are uneven to a certain extent, meets deep learning model full-page proof
The requirement of this amount, can effectively avoid the model trained from over-fitting occur, and the situation of generalization ability deficiency improves assistant analysis
As a result reliability.Before core of the invention is to trained deep learning model is applied to after the progress stochastic transformation of image fritter
Picture pretreatment link in, effectively solves the problems, such as that pathological section figure is excessive and sample size deficiency.
Detailed description of the invention
Now in conjunction with attached drawing, the present invention is further elaborated:
Fig. 1 is that the present invention is based on the flow charts of liver's pathological image sample Enhancement Method of stochastic transformation；
Fig. 2 is that the present invention is based on the random variation schematic diagrames of liver's pathological image sample Enhancement Method of stochastic transformation.
Specific embodiment
As shown in Fig. 1 or Fig. 2, the present invention is based on liver's pathological image sample Enhancement Methods of stochastic transformation comprising
Following steps:
1) block division is carried out to liver's pathological image, obtains several image fritters；
2) stochastic transformation is carried out to each image fritter, forms exptended sample；The stochastic transformation include horizontal mirror image switch,
One or more of vertical mirror overturning, cutting, brightness adjustment, saturation degree adjustment and adjustment of color；
3) exptended sample input deep learning model is trained liver's pathological image, and is carried out pair with liver pathological image
It should enhance, obtain the enhancing sample of liver's pathological image.
Preferably, the size of image fritter described in step 1) is 320*320 pixel.
Preferably, the method for horizontal mirror image switch described in step 2) and vertical mirror overturning are as follows:
If the width of image fritter is width, length height, the former coordinate in image fritter before a certain point transformation is
(x0, y0), transformed coordinate is (x, y), then,
Horizontal mirror image switch needs to meet:
X=width-x0-1
Y=y0；
Vertical mirror overturning, needs to meet:
Preferably, the method for cutting described in step 2) are as follows: keep the resolution ratio of image fritter constant, if image fritter
Width be width, length height, the cutting coefficient of width and length is respectively a and b, then, the image after cutting is wide
Degree is width*a, length height*b.
Preferably, the method for brightness adjustment described in step 2), saturation degree adjustment and adjustment of color are as follows:
HSI is converted by image fritter rgb pixel value, conversion formula is as follows:
Wherein,
Saturation degree component is given by:
Strength component is given by:
The change coefficient of HSI three is both configured to 0.1, the HSI of image fritter is changed into original 0.9 or 1.1 at random
Times, complete the adjustment to the small Block Brightness of image, saturation degree or tone.
Embodiment
The present invention is based on liver's pathological image sample Enhancement Methods of stochastic transformation comprising following steps:
The division of Step1, image block
Pathological image usually has high resolution ratio, if directly operating to full figure can allow computational efficiency extremely low.Cause
Suitable piecemeal length is arranged first according to the resolution ratio of full scan pathological image in this, carries out part point to whole pathological image
Block.For liver's pathological image, the block size selected is 320*320 pixel, altogether obtain wn*hn fritter, piecemeal simultaneously,
Record the position of each fritter.
Step 2, sample enhancing
(1) mirror image switch (horizontal direction or vertical direction)
The horizontal and vertical mirror transformation principle of image fritter is as follows: setting the width of image as width, length height；(x,
It y) is the transformed coordinate in certain point in image, (x0, y0) for its transformation before former coordinate；So,
Horizontal mirror image switch needs to meet:
X=width-x0-1
Y=y0
Vertical mirror overturning needs to meet:
(2) random cropping
It keeps the resolution ratio of original image constant, takes out the treatment process of a part of content in original image, if image fritter
Cutting is exactly according to cutting coefficient a, b, and the width of image fritter is width, length height, then after cutting image width
Degree is width*a, length height*b.
(3) brightness, saturation degree and tone are adjusted
Usually, the tone (Hue) of image, saturation degree (Saturation) and brightness (Intensity) are referred to as HSI, are
Another describing mode of RGB image adjusts image HIS, needs the pixel value by image RGB to be converted into HSI, conversion formula is such as
Under:
Wherein,
Saturation degree component is given by:
Last strength component is given by:
In liver's pathological picture, the change coefficient of HSI three is both configured to 0.1, the HSI of original image is changed at random
Originally 0.9 or 1.1 times, complete the adjustment to brightness of image, saturation degree and tone.
Above description should not have any restriction to protection scope of the present invention.
Claims (5)
1. a kind of liver's pathological image sample Enhancement Method based on stochastic transformation, it is characterised in that: itself the following steps are included:
1) block division is carried out to liver's pathological image, obtains several image fritters；
2) stochastic transformation is carried out to each image fritter, forms exptended sample；The stochastic transformation include horizontal mirror image switch,
One or more of vertical mirror overturning, cutting, brightness adjustment, saturation degree adjustment and adjustment of color；
3) exptended sample input deep learning model is trained liver's pathological image, and is carried out pair with liver pathological image
It should enhance, obtain the enhancing sample of liver's pathological image.
2. a kind of liver's pathological image sample Enhancement Method based on stochastic transformation according to claim 1, feature exist
In: the size of image fritter described in step 1) is 320*320 pixel.
3. a kind of liver's pathological image sample Enhancement Method based on stochastic transformation according to claim 1, feature exist
In: the method for horizontal mirror image switch described in step 2) and vertical mirror overturning are as follows:
If the width of image fritter is width, length height, the former coordinate in image fritter before a certain point transformation is (x0,
y0), transformed coordinate is (x, y), then,
Horizontal mirror image switch needs to meet:
X=width-x0-1
Y=y0；
Vertical mirror overturning, needs to meet:
4. a kind of liver's pathological image sample Enhancement Method based on stochastic transformation according to claim 1, feature exist
In: the method for cutting described in step 2) are as follows: keep the resolution ratio of image fritter constant, if the width of image fritter is width,
Length is height, and the cutting coefficient of width and length is respectively a and b, then, the picture traverse after cutting is width*a, long
Degree is height*b.
5. a kind of liver's pathological image sample Enhancement Method based on stochastic transformation according to claim 1, feature exist
In: brightness adjustment described in step 2), saturation degree adjust and the method for adjustment of color are as follows:
HSI is converted by image fritter rgb pixel value, conversion formula is as follows:
Wherein,
Saturation degree component is given by:
Strength component is given by:
The change coefficient of HSI three is both configured to 0.1, the HSI of image fritter is changed into original 0.9 or 1.1 at random
Times, complete the adjustment to the small Block Brightness of image, saturation degree or tone.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910526943.0A CN110288542A (en) | 2019-06-18 | 2019-06-18 | A kind of liver's pathological image sample Enhancement Method based on stochastic transformation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910526943.0A CN110288542A (en) | 2019-06-18 | 2019-06-18 | A kind of liver's pathological image sample Enhancement Method based on stochastic transformation |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110288542A true CN110288542A (en) | 2019-09-27 |
Family
ID=68004191
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910526943.0A Pending CN110288542A (en) | 2019-06-18 | 2019-06-18 | A kind of liver's pathological image sample Enhancement Method based on stochastic transformation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110288542A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110866893A (en) * | 2019-09-30 | 2020-03-06 | 中国科学院计算技术研究所 | Pathological image-based TMB classification method and system and TMB analysis device |
| CN111062956A (en) * | 2019-11-08 | 2020-04-24 | 哈尔滨工业大学（深圳） | Pathological image lump target segmentation method and device |
| CN113011468A (en) * | 2021-02-25 | 2021-06-22 | 上海皓桦科技股份有限公司 | Image feature extraction method and device |
| GB2591177A (en) * | 2019-11-21 | 2021-07-21 | Hsiao Ching Nien | Method and apparatus of intelligent analysis for liver tumour |
| CN116779170A (en) * | 2023-08-24 | 2023-09-19 | 济南市人民医院 | Pulmonary function attenuation prediction system and device based on self-adaptive deep learning |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPWO2012157201A1 (en) * | 2011-05-18 | 2014-07-31 | 日本電気株式会社 | Information processing system, information processing method, information processing apparatus, control method thereof, and control program |
| CN109740626A (en) * | 2018-11-23 | 2019-05-10 | 杭州电子科技大学 | The detection method of cancerous area in breast cancer pathological section based on deep learning |
| CN109740669A (en) * | 2018-12-29 | 2019-05-10 | 大连大学 | A kind of breast cancer pathology image classification method based on depth characteristic polymerization |
-
2019
- 2019-06-18 CN CN201910526943.0A patent/CN110288542A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPWO2012157201A1 (en) * | 2011-05-18 | 2014-07-31 | 日本電気株式会社 | Information processing system, information processing method, information processing apparatus, control method thereof, and control program |
| CN109740626A (en) * | 2018-11-23 | 2019-05-10 | 杭州电子科技大学 | The detection method of cancerous area in breast cancer pathological section based on deep learning |
| CN109740669A (en) * | 2018-12-29 | 2019-05-10 | 大连大学 | A kind of breast cancer pathology image classification method based on depth characteristic polymerization |
Non-Patent Citations (2)
| Title |
|---|
| 余胜威等: "《MATLAB图像滤波去噪分析及其应用》", 30 September 2015 * |
| 李琼等: "糖尿病性视网膜图像的深度学习分类方法", 《中国图象图形学报》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110866893A (en) * | 2019-09-30 | 2020-03-06 | 中国科学院计算技术研究所 | Pathological image-based TMB classification method and system and TMB analysis device |
| CN110866893B (en) * | 2019-09-30 | 2021-04-06 | 中国科学院计算技术研究所 | Pathological image-based TMB classification method and system and TMB analysis device |
| US11468565B2 (en) | 2019-09-30 | 2022-10-11 | Institute Of Computing Technology, Chinese Academy Of Sciences | TMB classification method and system and TMB analysis device based on pathological image |
| CN111062956A (en) * | 2019-11-08 | 2020-04-24 | 哈尔滨工业大学（深圳） | Pathological image lump target segmentation method and device |
| GB2591177A (en) * | 2019-11-21 | 2021-07-21 | Hsiao Ching Nien | Method and apparatus of intelligent analysis for liver tumour |
| CN113011468A (en) * | 2021-02-25 | 2021-06-22 | 上海皓桦科技股份有限公司 | Image feature extraction method and device |
| CN116779170A (en) * | 2023-08-24 | 2023-09-19 | 济南市人民医院 | Pulmonary function attenuation prediction system and device based on self-adaptive deep learning |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110288542A (en) | A kind of liver's pathological image sample Enhancement Method based on stochastic transformation | |
| EP2546802B1 (en) | Generating artificial hyperspectral images using correlated analysis of co-registered images | |
| CN101576997B (en) | Abdominal organ segmentation method based on secondary three-dimensional region growth | |
| Wu et al. | Prediction of molecular subtypes of breast cancer using BI-RADS features based on a “white box” machine learning approach in a multi-modal imaging setting | |
| Provost et al. | Comparison of conventional photographs and telephonically transmitted compressed digitized images of melanomas and dysplastic nevi | |
| US20040264749A1 (en) | Boundary finding in dermatological examination | |
| CN110223303A (en) | HE dyes organ pathological image dividing method, device | |
| CN110276761A (en) | A kind of liver's histopathology image partition method based on OTSU threshold value | |
| CN106447682A (en) | Automatic segmentation method for breast MRI focus based on Inter-frame correlation | |
| CN111798425B (en) | Intelligent detection method for mitotic image in gastrointestinal stromal tumor based on deep learning | |
| CN109410194A (en) | A kind of cancer of the esophagus pathology image processing method based on deep learning | |
| CN111986150A (en) | Interactive marking refinement method for digital pathological image | |
| CN109712142A (en) | Based on the isolated breast cancer Ki67/ER/PR caryochrome method of counting of dyeing | |
| CN109363699A (en) | A kind of method and device of breast image lesion identification | |
| CN107122597B (en) | Intelligent diagnosis system for corneal damage | |
| EP3271864B1 (en) | Tissue sample analysis technique | |
| CN109785943A (en) | A kind of monitoring of pathology and diagnostic message processing system and method | |
| CN112263217B (en) | Improved convolutional neural network-based non-melanoma skin cancer pathological image lesion area detection method | |
| CN109363697A (en) | A kind of method and device of breast image lesion identification | |
| CN107392893A (en) | Tissue density's analysis method and system | |
| CN108961222A (en) | A kind of cervical carcinoma early screening recognition methods based on gynecatoptron image | |
| CN105869169B (en) | A kind of automatic division method of the micro- arrangement image of tumor tissues | |
| Peng et al. | Segmentation of prostatic glands in histology images | |
| CN109685783A (en) | A kind of method for cell count based on skeletal extraction | |
| CN106529420B (en) | The optic disk center positioning method of comprehensive eye fundus image marginal information and luminance information |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190927 |