A novel computational method for automatic segmentation, quantification and comparative analysis of immunohistochemically labeled tissue sections

Casiraghi, Elena; Huber, Veronica; Frasca, Marco; Cossa, Mara; Tozzi, Matteo; Rivoltini, Licia; Leone, Biagio Eugenio; Villa, Antonello; Vergani, Barbara

doi:10.1186/s12859-018-2302-3

Cited by 18 publications

(12 citation statements)

References 60 publications

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“…20,21 Additionally, MATLAB software ( MIAQuant code) was recently reported as novel computational method for the quantification of IHC stained tissue sections. 23,24 This software is already used in clinical cancer research for determination of the expression of microRNAs in melanomas, which might have value in prediction of poor immunotherapy outcome. 25 As no discrimination could be made between tumour epithelium and non-epithelial tissue, this method is not suitable for quantification of tumour biomarkers that are also expressed by non-epithelial cells.…”

Section: Discussionmentioning

confidence: 99%

A method for semi-automated image analysis of HLA class I tumour epithelium expression in rectal cancer

et al. 2019

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Biomarkers may hold the key towards development and improvement of personalized cancer treatment. For instance, tumour expression of immune system-related proteins may reveal the tumour immune status and, accordingly, determine choice for type of immunotherapy. Therefore, objective evaluation of tumour biomarker expression is needed but often challenging. For instance, human leukocyte antigen (HLA) class I tumour epithelium expression is cumbersome to quantify by eye due to its presence on both tumour epithelial cells and tumour stromal cells, as well as tumour-infiltrating immune cells. In this study, we solved this problem by setting up an immunohistochemical (IHC) double staining using a tissue microarray (TMA) of rectal tumours wherein HLA class I expression was coloured with a blue chromogen, whereas non-epithelial tissue was visualized with a brown chromogen. We subsequently developed a semi-automated image analysis method that identified tumour epithelium as well as the percentage of HLA class I-positive tumour epithelium. Using this technique, we compared HCA2/HC10 and EMR8-5 antibodies for the assessment of HLA class I tumour expression and concluded that EMR8-5 is the superior antibody for this purpose. This IHC double staining can in principle be used for scoring of any biomarker expressed by tumour epithelium.

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Section: Discussionmentioning

confidence: 99%

A method for semi-automated image analysis of HLA class I tumour epithelium expression in rectal cancer

et al. 2019

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“…Other approaches have been using knn techniques as the final judges at the end of cascading systems, e.g. for the classification of diabetic patients [68] or for the classification of marker pixels [69] from histochemical images.…”

Section: ) Knn Classifiers and Support Vector Machines For Predictionmentioning

confidence: 99%

Human Digital Twin for Fitness Management

et al. 2020

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Our research work describes a team of human Digital Twins (DTs), each tracking fitness-related measurements describing an athlete's behavior in consecutive days (e.g. food income, activity, sleep). After collecting enough measurements, the DT firstly predicts the physical twin performance during training and, in case of non-optimal result, it suggests modifications in the athlete's behavior. The athlete's team is integrated into SmartFit, a software framework for supporting trainers and coaches in monitoring and manage athletes' fitness activity and results. Through IoT sensors embedded in wearable devices and applications for manual logging (e.g. mood, food income), SmartFit continuously captures measurements, initially treated as the dynamic data describing the current physical twins' status. Dynamic data allows adapting each DT's status and triggering the DT's predictions and suggestions. The analyzed measurements are stored as the historical data, further processed by the DT to update (increase) its knowledge and ability to provide reliable predictions. Results show that, thanks to the team of DTs, SmartFit computes trustable predictions of the physical twins' conditions and produces understandable suggestions which can be used by trainers to trigger optimization actions in the athletes' behavior. Though applied in the sport context, SmartFit can be easily adapted to other monitoring tasks. INDEX TERMS Counterfactual explanations, digital twins, Internet of Things, machine learning, smart health, sociotechnical design, wearables.

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“…in areas such as medicine and biology [3], [4], object recognition [5], time series prediction [6], [7] and industrial production [8]- [10], the application of machine learning techniques to the automated trading is not an easy thing [11]. Unlike common supervised learning methods, automated trading has no labels to learn, so it needs to continuously explore through an unknown environment and continuously update and optimize the decision through the feedback of the environment, and that is also the way to reinforcement learning [12].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Financial Trading System Using Deep Reinforcement Learning With Candlestick Decomposing Features

Ding

Luo

2020

IEEE Access

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When applying artificial intelligence technology to quantitative trading, high noise and unpredictability of market environment are the first practical problems to be considered. Therefore, how to select the learning features of the market based on rapidly changing financial data is particularly important. In this paper, the real time financial data are first processed by K-line theory, which uses candlesticks as a generalization of price movements over a period of time, so this process can play the role of de-noising. Then, the candlesticks are decomposed into different subparts by mean of a specified spatio-temporal relationship, based on which cluster analysis of the subparts to get the learning features. Further, the learning features that are clustered by the above K-lines are put into the model, and the online adaptive control of the parameters in the unknown environment is realized by the deep reinforcement learning method, so as to realize the high frequency transaction strategy. In order to verify the performance of the model, the data on different financial derivatives transactions such as stocks, financial futures and commodity futures are used. The proposal approach is compared with other methods which are based on price, fuzzified price and K-lines for features learning. In order to verify the accuracy of the proposal approach, prediction-based methods such as recurrent neural network and fuzzy neural network are used for comparison. Experimental results show that the proposed method has higher robustness and prediction accuracy. INDEX TERMS Deep reinforcement learning, K-line decomposing, clustering, trading system.

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A novel computational method for automatic segmentation, quantification and comparative analysis of immunohistochemically labeled tissue sections

Cited by 18 publications

References 60 publications

A method for semi-automated image analysis of HLA class I tumour epithelium expression in rectal cancer

A method for semi-automated image analysis of HLA class I tumour epithelium expression in rectal cancer

Human Digital Twin for Fitness Management

An Adaptive Financial Trading System Using Deep Reinforcement Learning With Candlestick Decomposing Features

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