A Hierarchical Convolutional Neural Network for vesicle fusion event classification

Li, Haohan; Mao, Yunxiang; Yin, Zhaozheng; Xu, Yingke

doi:10.1016/j.compmedimag.2017.04.003

Cited by 4 publications

(4 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The avarage precision determined on the same set is 96.33%. This is comparable to advanced methods based on deep networks analyzing image sequences, which allow, based on [27], a precision of 95.0% for full fusion and 96.7% for partial fusion, and based on [28], a precision of 95.2% for full fusion and 96.1% for partial fusion. These parameters were obtained using an analog implementation of a very simple two-layer network consisting of 10 neurons.…”

Section: Cmos Classifier Parametersmentioning

confidence: 53%

“…The first approach is based on the analysis of image sequence obtained using Total Internal Reflection Fluorescence (TIRF) Microscopy [26]. This approach uses machine learning methods mainly based on Convolutional Neural Network [27] or Hierarchical Convolutional Neural Network (HCNN) [28]. The precision of both types of networks in the task of classification of the full and the partial fusion is higher than 95%.…”

Section: Vesicle Fusionmentioning

confidence: 99%

See 1 more Smart Citation

CMOS Perceptron for Vesicle Fusion Classification

Naumowicz¹,

Pietrzak²,

Szczęsny³

et al. 2022

Electronics

View full text Add to dashboard Cite

Edge computing (processing data close to its source) is one of the fastest developing areas of modern electronics and hardware information technology. This paper presents the implementation process of an analog CMOS preprocessor for use in a distributed environment for processing medical data close to the source. The task of the circuit is to analyze signals of vesicle fusion, which is the basis of life processes in multicellular organisms. The functionality of the preprocessor is based on a classifier of full and partial fusions. The preprocessor is dedicated to operate in amperometric systems, and the analyzed signals are data from carbon nanotube electrodes. The accuracy of the classifier is at the level of 93.67%. The implementation was performed in the 65 nm CMOS technology with a 0.3 V power supply. The circuit operates in the weak-inversion mode and is dedicated to be powered by thermal cells of the human energy harvesting class. The maximum power consumption of the circuit equals 416 nW, which makes it possible to use it as an implantable chip. The results can be used, among others, in the diagnosis of precancerous conditions.

show abstract

Section: Cmos Classifier Parametersmentioning

confidence: 53%

Section: Vesicle Fusionmentioning

confidence: 99%

CMOS Perceptron for Vesicle Fusion Classification

Naumowicz¹,

Pietrzak²,

Szczęsny³

et al. 2022

Electronics

View full text Add to dashboard Cite

show abstract

“…In turn methods based on the Gaussian Mixture Model [29] feature a level of precision of 77.0% for the full fusion and 75.5% for the partial fusion. Using the Gaussian Mixture Model together with the Hierarchical Convolutional Neural Network (HCNN) allows the precision to be improved, which in this case based on [30] is: 95.2% for full fusion and 96.1% for partial fusion. In our approach described in this paper, we used data analysis using modern nanoscale sensors.…”

Section: Related Workmentioning

confidence: 99%

Exocytotic vesicle fusion classification for early disease diagnosis using a mobile GPU microsystem

Szczęsny

Pietrzak

2021

Neural Comput & Applic

View full text Add to dashboard Cite

This work addresses monitoring vesicle fusions occurring during the exocytosis process, which is the main way of intercellular communication. Certain vesicle behaviors may also indicate certain precancerous conditions in cells. For this purpose we designed a system able to detect two main types of exocytosis: a full fusion and a kiss-and-run fusion, based on data from multiple amperometric sensors at once. It uses many instances of small perceptron neural networks in a massively parallel manner and runs on Jetson TX2 platform, which uses a GPU for parallel processing. Based on performed benchmarking, approximately 140,000 sensors can be processed in real time within the sensor sampling period equal to 10 ms and an accuracy of 99$$\%$$ % . The work includes an analysis of the system performance with varying neural network sizes, input data sizes, and sampling periods of fusion signals.

show abstract

“…The classification model proposed in this paper makes use of deep convolutional neural networks (CNNs) for their state‐of‐the‐art performance in other image classification problems [6–10]. Transfer learning was applied to overcome the limited amount of training data that are available and score‐based fusion was used for classifier combination of multiple CNNs as well as traditional hand‐crafted features to further improve performance.…”

Section: Introductionmentioning

confidence: 99%