Automated Analysis and Display of Temporal Sequences of Mammograms

2004 ◽  
Author(s):  
Walter F. Good
Keyword(s):  
Automated Analysis ◽  
Temporal Sequences

Development and study of a method for cell separation during white blood cell segmentation on images of bone marrow preparations in information and measurement systems for diagnostics of acute leukemia

2020 ◽  
pp. 68-72
Author(s):  
V.G. Nikitaev ◽  
A.N. Pronichev ◽  
V.V. Dmitrieva ◽  
E.V. Polyakov ◽  
A.D. Samsonova ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Blood Cell ◽  
Acute Leukemia ◽  
White Blood Cell ◽  
Cell Separation ◽  
Large Scale ◽  
Bone Marrow Cells ◽  
Automated Analysis ◽  
Cell Segmentation ◽  
Measurement Systems

The issues of using of information and measurement systems based on processing of digital images of microscopic preparations for solving large-scale tasks of automating the diagnosis of acute leukemia are considered. The high density of leukocyte cells in the preparation (hypercellularity) is a feature of microscopic images of bone marrow preparations. It causes the proximity of cells to eachother and their contact with the formation of conglomerates. Measuring of the characteristics of bone marrow cells in such conditions leads to unacceptable errors (more than 50%). The work is devoted to segmentation of contiguous cells in images of bone marrow preparations. A method of cell separation during white blood cell segmentation on images of bone marrow preparations under conditions of hypercellularity of the preparation has been developed. The peculiarity of the proposed method is the use of an approach to segmentation of cell images based on the watershed method with markers. Key stages of the method: the formation of initial markers and builds the lines of watershed, a threshold binarization, shading inside the outline. The parameters of the separation of contiguous cells are determined. The experiment confirmed the effectiveness of the proposed method. The relative segmentation error was 5 %. The use of the proposed method in information and measurement systems of computer microscopy for automated analysis of bone marrow preparations will help to improve the accuracy of diagnosis of acute leukemia.

Software including the functions of automated analysis of electric power consumption in pipeline oil transportation

2011 ◽  
Vol 8 (1) ◽  
pp. 233-238
Author(s):  
R.M. Bogdanov ◽  
S.V. Lukin
Keyword(s):  
Power Consumption ◽  
Electric Power ◽  
Data Model ◽  
Automated Analysis ◽  
Petroleum Products ◽  
Electric Power Consumption ◽  
Scientific Center ◽  
Relational Data Model ◽  
Oil Transportation ◽  
Academy Of Sciences

Oil and petroleum products transportation is characterized by a significant cost of electric power. Correct oil and petroleum products accounting and forecasting requires knowledge of many factors. The software for norms of electric power consumption analysis for the planned period was developed at the Ufa Scientific Center of the Russian Academy of Sciences. Based on the principles of the relational data model, a schematic diagram/arrangement for the main oil transportation objects was developed, which allows to hold the initial data and calculated parameters in a structured manner.

Advances in the Prediction of Protein Subcellular Locations with Machine Learning

2019 ◽  
Vol 14 (5) ◽  
pp. 406-421 ◽  
Author(s):  
Ting-He Zhang ◽  
Shao-Wu Zhang
Keyword(s):  
Machine Learning ◽  
Feature Fusion ◽  
Protein Sequences ◽  
Subcellular Location ◽  
Automated Analysis ◽  
Cellular Level ◽  
Machine Learning Algorithms ◽  
Feature Representation ◽  
Protein Subcellular Location ◽  
Protein Subcellular Locations

Background: Revealing the subcellular location of a newly discovered protein can bring insight into their function and guide research at the cellular level. The experimental methods currently used to identify the protein subcellular locations are both time-consuming and expensive. Thus, it is highly desired to develop computational methods for efficiently and effectively identifying the protein subcellular locations. Especially, the rapidly increasing number of protein sequences entering the genome databases has called for the development of automated analysis methods. Methods: In this review, we will describe the recent advances in predicting the protein subcellular locations with machine learning from the following aspects: i) Protein subcellular location benchmark dataset construction, ii) Protein feature representation and feature descriptors, iii) Common machine learning algorithms, iv) Cross-validation test methods and assessment metrics, v) Web servers. Result & Conclusion: Concomitant with a large number of protein sequences generated by highthroughput technologies, four future directions for predicting protein subcellular locations with machine learning should be paid attention. One direction is the selection of novel and effective features (e.g., statistics, physical-chemical, evolutional) from the sequences and structures of proteins. Another is the feature fusion strategy. The third is the design of a powerful predictor and the fourth one is the protein multiple location sites prediction.

Mining Social Media Data to Study the Consequences of Dementia Diagnosis on Caregivers and Relatives (Preprint)

2018 ◽  
Author(s):  
Anika Oellrich ◽  
George Gkotsis ◽  
Richard James Butler Dobson ◽  
Tim JP Hubbard ◽  
Rina Dutta
Keyword(s):  
Social Media ◽  
Family Relationships ◽  
Text Processing ◽  
Automated Analysis ◽  
Health Concern ◽  
Dementia Diagnosis ◽  
Data Set ◽  
Social Media Data ◽  
Real Time Processing ◽  
Media Data

BACKGROUND Dementia is a growing public health concern with approximately 50 million people affected worldwide in 2017 and this number is expected to reach more than 131 million by 2050. The toll on caregivers and relatives cannot be underestimated as dementia changes family relationships, leaves people socially isolated, and affects the finances of all those involved. OBJECTIVE The aim of this study was to explore using automated analysis (i) the age and gender of people who post to the social media forum Reddit about dementia diagnoses, (ii) the affected person and their diagnosis, (iii) relevant subreddits authors are posting to, (iv) the types of messages posted and (v) the content of these posts. METHODS We analysed Reddit posts concerning dementia diagnoses. We used a previously developed text analysis pipeline to determine attributes of the posts as well as their authors to characterise online communications about dementia diagnoses. The posts were also examined by manual curation for the diagnosis provided and the person affected. Furthermore, we investigated the communities these people engage in and assessed the contents of the posts with an automated topic gathering technique. RESULTS Our results indicate that the majority of posters in our data set are women, and it is mostly close relatives such as parents and grandparents that are mentioned. Both the communities frequented and topics gathered reflect not only the sufferer's diagnosis but also potential outcomes, e.g. hardships experienced by the caregiver. The trends observed from this dataset are consistent with findings based on qualitative review, validating the robustness of social media automated text processing. CONCLUSIONS This work demonstrates the value of social media data sources as a resource for in-depth studies of those affected by a dementia diagnosis and the potential to develop novel support systems based on their real time processing in line with the increasing digitalisation of medical care.

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