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Classification and Fast Few-Shot Learning of Steel Surface Defects with Randomized Network

Amr M. Nagy, László Czúni
2022 Applied Sciences  
However, in real-life applications new types of errors can always appear, thus incremental learning, based on very few example shots, is challenging.  ...  The classification outperforms all other known approaches, with an accuracy 100% or almost 100%, on the two datasets with the off-the-shelf network.  ...  In CPN, the image is first classified by a multi-group CNN, training different groups of convolution kernels separately to extract the feature map groups of different types of defects.  ... 
doi:10.3390/app12083967 fatcat:7ucjm34wgvabrf7nvnkaryej5e

Deep neural network models for computational histopathology: A survey [article]

Chetan L. Srinidhi, Ozan Ciga, Anne L. Martel
2019 arXiv   pre-print
We also provide an overview of deep learning based survival models that are applicable for disease-specific prognosis tasks.  ...  Histopathological images contain rich phenotypic information that can be used to monitor underlying mechanisms contributing to diseases progression and patient survival outcomes.  ...  Extracted features using pre-trained VGG-16.  ... 
arXiv:1912.12378v1 fatcat:xdfkzzwzb5alhjfhffqpcurb2u

Deep Learning Methods for Accurate Skin Cancer Recognition and Mobile Application

Ioannis Kousis, Isidoros Perikos, Ioannis Hatzilygeroudis, Maria Virvou
2022 Electronics  
In conclusion, we achieved state-of-the-art results in skin cancer recognition based on a single, relatively light deep learning model, which we also used in a mobile application.  ...  The application can also inform the user about the allowed sun exposition time based on the current UV radiation degree, the phototype of the user's skin and the degree of the used sunscreen.  ...  The authors in [35] built an architecture combining the MobileNet and LSTM architectures for skin disease classification. They used the HAM10000 dataset (seven classes) for training and evaluation.  ... 
doi:10.3390/electronics11091294 fatcat:matscm757bhodgpdcetqn5flri

Horizons in Single-Lead ECG Analysis From Devices to Data

Abdelrahman Abdou, Sridhar Krishnan
2022 Frontiers in Signal Processing  
Trends in ECG de-noising, signal processing, feature extraction, compressive sensing (CS), and remote monitoring applications are later followed to show the emerging opportunities and recent innovations  ...  TRENDS IN FEATURE EXTRACTION FROM ECG DATA An input signal can contain many features that make it unique and can be used in classification, and analysis easily.  ...  The above three approaches use the classical Nyquist sampling rate and requires the full signal for feature extraction.  ... 
doi:10.3389/frsip.2022.866047 fatcat:tytv4grdgbai5bqh7gcvcr72ae

Multiplexed Immunohistochemistry and Digital Pathology as the Foundation for Next-Generation Pathology in Melanoma: Methodological Comparison and Future Clinical Applications

Yannick Van Herck, Asier Antoranz, Madhavi Dipak Andhari, Giorgia Milli, Oliver Bechter, Frederik De Smet, Francesca Maria Bosisio
2021 Frontiers in Oncology  
The state-of-the-art for melanoma treatment has recently witnessed an enormous revolution, evolving from a chemotherapeutic, "one-drug-for-all" approach, to a tailored molecular- and immunological-based  ...  approach with the potential to make personalized therapy a reality.  ...  of melanocytic tumor on skin whole slide biopsy images. 66 H&E stained skin WSIs including 17 normal skin tissues, 17 nevi and 32 melanomas multi-class support vector machine (mSVM) with extracted  ... 
doi:10.3389/fonc.2021.636681 pmid:33854972 pmcid:PMC8040928 fatcat:vqpqkemqp5bpnl6d66k4hmvtkm

Deep Learning for Visual Speech Analysis: A Survey [article]

Changchong Sheng, Gangyao Kuang, Liang Bai, Chenping Hou, Yulan Guo, Xin Xu, Matti Pietikäinen, Li Liu
2022 arXiv   pre-print
Over the past five years, numerous deep learning based methods have been proposed to address various problems in this area, especially automatic visual speech recognition and generation.  ...  From the feature engineering perspective, traditional feature extraction methods can be categorized into three types: appearance-based, shapebased, and motion-based [8] .  ...  [43] conducted a survey reviewing deep learning driven VSR methods, including audio-visual datasets, feature extraction, classification networks and classification schemes.  ... 
arXiv:2205.10839v1 fatcat:l5m4ohtcvnevrliaiwawg3phjq

Program

2021 2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)  
In this paper, we proposed an improved emotion classification system based on a LRCN model using EEG signals and reinforced with a fuzzification process on extracted ECG and PPG features  ...  Using both the text features extracted from text data and visual features extracted from distress images realizes the performance improvement of degradation level estimation.  ... 
doi:10.1109/icce-tw52618.2021.9602919 fatcat:aetmvxb7hfah7iuucbamos2wgu

CARS 2021: Computer Assisted Radiology and Surgery Proceedings of the 35th International Congress and Exhibition Munich, Germany, June 21–25, 2021

2021 International Journal of Computer Assisted Radiology and Surgery  
The multi-step extraction method used for large-intestine extraction is a method for detecting the optimal shape based on the set features by binarization while gradually changing the threshold from a  ...  In the first stage, we extract features for classification from input images. The method of feature extraction is described below.  ... 
doi:10.1007/s11548-021-02375-4 pmid:34085172 fatcat:6d564hsv2fbybkhw4wvc7uuxcy

Engineering, Technology & Applied Science Research (ETASR), Vol. 12, No. 2, pp. 8228-8481

Various
2022 Zenodo  
ACKNOWLEDGMENT We wish to thank the car owners who allowed us to take photos of their cars, the completion of our study would not have been possible without their support.  ...  Several feature extraction methods are used to obtain significant features that can be used in the disease classification stage.  ...  Classification Method Based on Machine Learning Classification is a supervised ML approach used in many applications such as disease prediction, classification, etc.  ... 
doi:10.5281/zenodo.6509470 fatcat:nn7pi5ec35gzplz6ewln2lbbrm

Motivation detection using EEG signal analysis by residual-in-residual convolutional neural network

Soham Chattopadhyay, Laila Zary, Chai Quek, Dilip K. Prasad
2021 Expert systems with applications  
Here we present a game-based motivation detection approach from the EEG signals.  ...  We take an original approach of using EEG-based brain computer interface to assess if motivation state is manifest in physiological EEG signals as well, and what are suitable conditions in order to achieve  ...  Our unique game-based data mining approach is designed to extract multi-dimensional EEG signals in response to motivation stimuli such as reward and punishment.  ... 
doi:10.1016/j.eswa.2021.115548 fatcat:j5c4pffkyrebvjmphvh6maxrla

Program

2021 2021 National Conference on Communications (NCC)  
Nonetheless, not every problem can and should be solved using deep neural networks (DNNs).  ...  In this talk, I will present methods for combining DNNs with traditional model-based algorithms.  ...  The classification between normal and shouted speech is performed using a DNN based classifier.  ... 
doi:10.1109/ncc52529.2021.9530194 fatcat:ahdw5ezvtrh4nb47l2qeos3dwq

An Algorithm for Heart Rate Extraction from Acoustic Recordings at the Neck

2018 IEEE Transactions on Biomedical Engineering  
This paper presents a novel algorithm for reliably extracting the heart rate from such acoustic recordings, keeping in mind the constraints posed by the wearable technology.  ...  of 13 subjects with an approximate data length of 75 hours and achieves an accuracy of 94.34%, an RMS error of 3.96 bpm and a correlation coefficient of 0.93 with reference to a commercial device in use  ...  Although many of the approaches discussed above achieve high accuracy, this comes at the cost of high computational complexity of the feature extraction and classification process.  ... 
doi:10.1109/tbme.2018.2836187 pmid:29993496 fatcat:nkq3u752tbdnvbjkg4jicm45ai

Dual-modality endoscopic probe for tissue surface shape reconstruction and hyperspectral imaging enabled by deep neural networks

Jianyu Lin, Neil T. Clancy, Ji Qi, Yang Hu, Taran Tatla, Danail Stoyanov, Lena Maier-Hein, Daniel S. Elson
2018 Medical Image Analysis  
A CNN based super-resolution model, namely "super-spectral-resolution" network (SSRNet), has also been devel-oped to estimate pixel-level dense hypercubes from the endoscope cameras standard RGB images  ...  The probe, with a 2.1 mm diameter, enables the sys-tem to be used with endoscope working channels.  ...  Fast feature matching with epipolar constraints Based on the spot detection results, correspondences between the captured and reference SL images were found using a cus-tomized feature descriptor and epipolar  ... 
doi:10.1016/j.media.2018.06.004 pmid:29933116 fatcat:js3wsbruazgp3npo6tcwukgtxi

Wearable Affective Life-Log System for Understanding Emotion Dynamics in Daily Life [article]

Byung Hyung Kim, Sungho Jo
2019 arXiv   pre-print
Based on the long-term monitoring, the system analyzes how the contexts of the user's life affect his/her emotion changes.  ...  Past research on recognizing human affect has made use of a variety of physiological sensors in many ways.  ...  Most EEG-based emotion recognition systems have extracted and selected EEG-based features through electrode selection based on neuro-scienctific assumptions.  ... 
arXiv:1911.01072v2 fatcat:ztdwnjxdencgvbl2vple2d53sy

Volume 2 of the Proceedings of the joint 12th International Conference on Methods and Techniques in Behavioral Research and 6th Seminar on Behavioral Methods held online May 18-22 2022 [article]

Andrew Spink, Jarosław Barski, Anne-Marie Brouwer, Gernot Riedel, Annesha Sil
2022 figshare.com  
Acknowledgments The authors acknowledge Hope Tech™ for their generation of chainrings used in the study. Acknowledgement This  ...  Feature extraction was based on the step segmentation of this method, see Figure 1 . The steps (N=1736 after quality control) served as input for feature extraction.  ...  Classification accuracy reached when using only traditional features was 96.7% and combined with Granger Causality features classification accuracy was 98.7%.  ... 
doi:10.6084/m9.figshare.20066849.v2 fatcat:f7ndcnjqlfbr3ptetfjij4hehm
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