A fully-convolutional residual encoder-decoder neural network to localize breast cancer on histopathology images

Farajzadeh, Nacer; Sadeghzadeh, Nima; Hashemzadeh, Mahdi

doi:10.1016/j.compbiomed.2022.105698

Cited by 8 publications

(2 citation statements)

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“…AI and machine learning fields of study try to enable computers to mimic living species' neural systems so that they can also perceive and/or behave as living species do [30][31][32]. For example, an artificial spider walking like a real one [33], or an AI-based model locating cancer nuclei on histopathology images like an expert does [34].…”

Section: Introductionmentioning

confidence: 99%

“…AI-based diagnosis methods have seen particular attention in medical and healthcare systems as they can often become a life-saving tool for people [34][35][36][37][38][39][40]. For example, since data from Alzheimer's Disease (AD) are multimodal and time series in nature, it is a burden for specialists to handle all the data that their patients provide; thus, they may not be able to correctly decide on the outcomes [38].…”

Section: Introductionmentioning

confidence: 99%

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NSSI questionnaires revisited: A data mining approach to shorten the NSSI questionnaires

Farajzadeh

Sadeghzadeh

2023

PLoS ONE

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Background and objective Non-suicidal self-injury (NSSI) is a psychological disorder that the sufferer consciously damages their body tissues, often too severe that requires intensive care medicine. As some individuals hide their NSSI behaviors, other people can only identify them if they catch them while injuring, or via dedicated questionnaires. However, questionnaires are long and tedious to answer, thus the answers might be inconsistent. Hence, in this study for the first time, we abstracted a larger questionnaire (of 662 items in total) to own only 22 items (questions) via data mining techniques. Then, we trained several machine learning algorithms to classify individuals based on their answers into two classes. Methods Data from 277 previously-questioned participants is used in several data mining methods to select features (questions) that highly represent NSSI, then 245 different people were asked to participate in an online test to validate those features via machine learning methods. Results The highest accuracy and F1 score of the selected features–via the Genetics algorithm–are 80.0% and 74.8% respectively for a Random Forest algorithm. Cronbach’s alpha of the online test (validation on the selected features) is 0.82. Moreover, results suggest that an MLP can classify participants into two classes of NSSI Positive and NSSI Negative with 83.6% accuracy and 83.7% F1-score based on the answers to only 22 questions. Conclusion While previously psychologists used many combined questionnaires to see whether someone is involved in NSSI, via various data mining methods, the present study showed that only 22 questions are enough to predict if someone is involved or not. Then different machine learning algorithms were utilized to classify participants based on their NSSI behaviors, among which, an MLP with 10 hidden layers had the best performance.

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