Texture features based microscopic image classification of liver cellular granuloma using artificial neural networks

Shi, Fuqian; Chen, Gaoxiang; Wang, Yu; Yang, Ningning; Chen, Yating; Dey, Nilanjan; Sherratt, R. Simon

doi:10.1109/itaic.2019.8785563

Cited by 5 publications

(1 citation statement)

References 25 publications

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“…Using other specialized software like Strataquest ( TissueGnostics StrataQuest, 2021 ) and Cell Profiler ( Mcquin et al, 2018 ) it is then possible to identify mechanistic reasons for observed branching in pseudotime. In parallel, classification of ROIs in branched trajectories can be used to label ROIs for training convolutional neural networks to identify such clusters in routinely stained images ( Shi et al, 2019 ). These can be used as a powerful research or diagnostic tool ( Figure 1.4b ).…”

Section: Downstream Analysis Of Multi-omics Datamentioning

confidence: 99%

Spatially Resolved Immunometabolism to Understand Infectious Disease Progression

Tans

Dey

et al. 2021

Front. Microbiol.

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Infectious diseases, including those of viral, bacterial, fungal, and parasitic origin are often characterized by focal inflammation occurring in one or more distinct tissues. Tissue-specific outcomes of infection are also evident in many infectious diseases, suggesting that the local microenvironment may instruct complex and diverse innate and adaptive cellular responses resulting in locally distinct molecular signatures. In turn, these molecular signatures may both drive and be responsive to local metabolic changes in immune as well as non-immune cells, ultimately shaping the outcome of infection. Given the spatial complexity of immune and inflammatory responses during infection, it is evident that understanding the spatial organization of transcripts, proteins, lipids, and metabolites is pivotal to delineating the underlying regulation of local immunity. Molecular imaging techniques like mass spectrometry imaging and spatially resolved, highly multiplexed immunohistochemistry and transcriptomics can define detailed metabolic signatures at the microenvironmental level. Moreover, a successful complementation of these two imaging techniques would allow multi-omics analyses of inflammatory microenvironments to facilitate understanding of disease pathogenesis and identify novel targets for therapeutic intervention. Here, we describe strategies for downstream data analysis of spatially resolved multi-omics data and, using leishmaniasis as an exemplar, describe how such analysis can be applied in a disease-specific context.

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Section: Downstream Analysis Of Multi-omics Datamentioning

confidence: 99%

Spatially Resolved Immunometabolism to Understand Infectious Disease Progression

Tans

Dey

et al. 2021

Front. Microbiol.

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An Examination System to Classify the Breast Thermal Images into Early/Acute DCIS Class

Dey

Rajinikanth

Hassanien

2020

Lecture Notes in Networks and Systems

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Simplified inverse filter tracked affective acoustic signals classification incorporating deep convolutional neural networks

Kuang

Wang

et al. 2020

Applied Soft Computing

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Facial expressions, verbal, behavioral, such as limb movements, and physiological features are vital ways for affective human interactions. Researchers have given machines the ability to recognize affective communication through the above modalities in the past decades. In addition to facial expressions, changes in the level of sound, strength, weakness, and turbulence will also convey affective. Extracting affective feature parameters from the acoustic signals have been widely applied in customer service, education, and the medical field. In this research, an improved AlexNet-based deep convolutional neural network (A-DCNN) is presented for acoustic signal recognition. Firstly, preprocessed on signals using simplified inverse filter tracking (SIFT) and short-time Fourier transform (STFT), Mel frequency Cepstrum (MFCC) and waveformbased segmentation were deployed to create the input for the deep neural network (DNN), which was applied widely in signals preprocess for most neural networks. Secondly, acoustic signals were acquired from the public Ryerson Audio-Visual Database of Affective Speech and Song (RAVDESS) affective speech audio system. Through the acoustic signal preprocessing tools, the basic features of the kind of sound signals were calculated and extracted. The proposed DNN based on improved AlexNet has a 95.88% accuracy on classifying eight affective of acoustic signals. By comparing with some linear classifications, such as decision table (DT) and Bayesian inference (BI) and other deep neural networks, such as AlexNet+SVM, recurrent convolutional neural network (R-CNN), etc., the proposed method achieves high effectiveness on the accuracy (A), sensitivity (S1), positive predictive (PP), and f1-score (F1). Acoustic signals affective recognition and classification can be potentially applied in industrial product design through measuring consumers' affective responses to products; by collecting relevant affective sound data to understand the popularity of the product, and furthermore, to improve the product design and increase the market responsiveness.

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Texture features based microscopic image classification of liver cellular granuloma using artificial neural networks

Cited by 5 publications

References 25 publications

Spatially Resolved Immunometabolism to Understand Infectious Disease Progression

Spatially Resolved Immunometabolism to Understand Infectious Disease Progression

An Examination System to Classify the Breast Thermal Images into Early/Acute DCIS Class

Simplified inverse filter tracked affective acoustic signals classification incorporating deep convolutional neural networks

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