Towards more efficient CNN-based surgical tools classification using transfer learning

AbstractContext-aware system (CAS) is a system that can understand the context of a given situation and either share this context with other systems for their response or respond by itself. In surgery, these systems are intended to assist surgeons enhance the scheduling productivity of operating rooms (OR) and surgical teams, and promote a comprehensive perception and consciousness of the OR. Furthermore, the automated surgical tool classification in medical images is a real-time computerized assistance to the surgeons in conducting different operations. Moreover, deep learning has embroiled in every facet of life due to the availability of large datasets and the emergence of convolutional neural networks (CNN) that have paved the way for the development of different image related processes. The aim of this paper is to resolve the problem of unbalanced data in the publicly available Cholec80 laparoscopy video dataset, using multiple data augmentation techniques. Furthermore, we implement a fine-tuned CNN to tackle the automatic tool detection during a surgery, with prospective use in the teaching field, evaluating surgeons, and surgical quality assessment (SQA). The proposed method is evaluated on a dataset of 80 cholecystectomy videos (Cholec80 dataset). A mean average precision of 93.75% demonstrates the effectiveness of the proposed method, outperforming the other models significantly.

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Classification of UrbanSound8k: A Study Using Convolutional Neural Network and Multiple Data Augmentation Techniques

Soft Computing and its Engineering Applications - Communications in Computer and Information Science ◽

10.1007/978-981-16-0708-0_5 ◽

2021 ◽

pp. 52-64

Author(s):

Aamer Abdul Rahman ◽

J. Angel Arul Jothi

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Data Augmentation ◽

Multiple Data ◽

Augmentation Techniques

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RFaceID

Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies ◽

10.1145/3494985 ◽

2021 ◽

Vol 5 (4) ◽

pp. 1-21

Author(s):

Chengwen Luo ◽

Zhongru Yang ◽

Xingyu Feng ◽

Jin Zhang ◽

Hong Jia ◽

...

Keyword(s):

Face Recognition ◽

Data Augmentation ◽

Negative Impact ◽

Facial Recognition ◽

Recognition System ◽

Privacy Leakage ◽

Multiple Data ◽

Extensive Evaluation ◽

Face Recognition System ◽

Augmentation Techniques

Face recognition (FR) has been widely used in many areas nowadays. However, the existing mainstream vision-based facial recognition has limitations such as vulnerability to spoofing attacks, sensitivity to lighting conditions, and high risk of privacy leakage, etc. To address these problems, in this paper we take a sparkly different approach and propose RFaceID, a novel RFID-based face recognition system. RFaceID only needs the users to shake their faces in front of the RFID tag matrix for a few seconds to get their faces recognized. Through theoretical analysis and experiment validations, the feasibility of the RFID-based face recognition is studied. Multiple data processing and data augmentation techniques are proposed to minimize the negative impact of environmental noises and user dynamics. A deep neural network (DNN) model is designed to characterize both the spatial and temporal feature of face shaking events. We implement the system and extensive evaluation results show that RFaceID achieves a high face recognition accuracy at 93.1% for 100 users, which shows the potential of RFaceID for future facial recognition applications.

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Classificação Morfológica de Galáxias Por Meio de Redes Neurais

10.5753/eri-mt.2019.8590 ◽

2019 ◽

Author(s):

Matheus Silva ◽

Thiago Ventura

Keyword(s):

Neural Network ◽

Data Augmentation ◽

Tuning Fork ◽

Morphological Classification ◽

Average Precision ◽

Optical Images ◽

Augmentation Techniques ◽

Mass Identification ◽

Data Variation

This paper proposes the development of a convolutional neural network for the morphological classification of galaxies through optical images, classifying them into six distinct classes based on the Hubble Tuning Fork model. In order to automate the mass identification and separation of the huge volume of data generated in recent astronomical observatories, deep learning and data augmentation techniques are used to generate increased data variation and consequently improve network accuracy. Our model achieved an average precision of 88%.

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Algorithms for Rule Generation and Matchmaking in Context-Aware System

Journal of Software ◽

10.3724/sp.j.1001.2009.03436 ◽

2009 ◽

Vol 20 (10) ◽

pp. 2655-2666 ◽

Cited By ~ 5

Author(s):

Dong LIU ◽

Xiang-Wu MENG ◽

Jun-Liang CHEN ◽

Ya-Mei XIA

Keyword(s):

Context Aware ◽

Rule Generation ◽

Context Aware System

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The Data-augmentation Techniques in Item Response Modeling: Current Approaches and New Developments

Advances in Psychological Science ◽

10.3724/sp.j.1042.2014.01036 ◽

2014 ◽

Vol 22 (6) ◽

pp. 1036

Author(s):

Wei TIAN ◽

Tao XIN ◽

Chunhua KANG

Keyword(s):

Item Response ◽

Data Augmentation ◽

New Developments ◽

Item Response Modeling ◽

Augmentation Techniques ◽

Response Modeling

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Automatic Labeled Dialogue Generation for Nursing Record Systems

Journal of Personalized Medicine ◽

10.3390/jpm10030062 ◽

2020 ◽

Vol 10 (3) ◽

pp. 62

Author(s):

Tittaya Mairittha ◽

Nattaya Mairittha ◽

Sozo Inoue

Keyword(s):

Data Augmentation ◽

Short Term Memory ◽

Generative Models ◽

Abstract Knowledge ◽

Augmentation Techniques ◽

Nursing Record ◽

Long Short Term Memory ◽

The Individual ◽

High Level ◽

Embedding Methods

The integration of digital voice assistants in nursing residences is becoming increasingly important to facilitate nursing productivity with documentation. A key idea behind this system is training natural language understanding (NLU) modules that enable the machine to classify the purpose of the user utterance (intent) and extract pieces of valuable information present in the utterance (entity). One of the main obstacles when creating robust NLU is the lack of sufficient labeled data, which generally relies on human labeling. This process is cost-intensive and time-consuming, particularly in the high-level nursing care domain, which requires abstract knowledge. In this paper, we propose an automatic dialogue labeling framework of NLU tasks, specifically for nursing record systems. First, we apply data augmentation techniques to create a collection of variant sample utterances. The individual evaluation result strongly shows a stratification rate, with regard to both fluency and accuracy in utterances. We also investigate the possibility of applying deep generative models for our augmented dataset. The preliminary character-based model based on long short-term memory (LSTM) obtains an accuracy of 90% and generates various reasonable texts with BLEU scores of 0.76. Secondly, we introduce an idea for intent and entity labeling by using feature embeddings and semantic similarity-based clustering. We also empirically evaluate different embedding methods for learning good representations that are most suitable to use with our data and clustering tasks. Experimental results show that fastText embeddings produce strong performances both for intent labeling and on entity labeling, which achieves an accuracy level of 0.79 and 0.78 f1-scores and 0.67 and 0.61 silhouette scores, respectively.

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Generative Adversarial Networks to Improve the Robustness of Visual Defect Segmentation by Semantic Networks in Manufacturing Components

Applied Sciences ◽

10.3390/app11146368 ◽

2021 ◽

Vol 11 (14) ◽

pp. 6368

Author(s):

Fátima A. Saiz ◽

Garazi Alfaro ◽

Iñigo Barandiaran ◽

Manuel Graña

Keyword(s):

Ad Hoc ◽

Data Augmentation ◽

Semantic Network ◽

Semantic Networks ◽

Stereo Image ◽

Generative Adversarial Networks ◽

Specific Class ◽

Adversarial Networks ◽

Augmentation Techniques ◽

Image Acquisition System

This paper describes the application of Semantic Networks for the detection of defects in images of metallic manufactured components in a situation where the number of available samples of defects is small, which is rather common in real practical environments. In order to overcome this shortage of data, the common approach is to use conventional data augmentation techniques. We resort to Generative Adversarial Networks (GANs) that have shown the capability to generate highly convincing samples of a specific class as a result of a game between a discriminator and a generator module. Here, we apply the GANs to generate samples of images of metallic manufactured components with specific defects, in order to improve training of Semantic Networks (specifically DeepLabV3+ and Pyramid Attention Network (PAN) networks) carrying out the defect detection and segmentation. Our process carries out the generation of defect images using the StyleGAN2 with the DiffAugment method, followed by a conventional data augmentation over the entire enriched dataset, achieving a large balanced dataset that allows robust training of the Semantic Network. We demonstrate the approach on a private dataset generated for an industrial client, where images are captured by an ad-hoc photometric-stereo image acquisition system, and a public dataset, the Northeastern University surface defect database (NEU). The proposed approach achieves an improvement of 7% and 6% in an intersection over union (IoU) measure of detection performance on each dataset over the conventional data augmentation.

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