On-device Training for Breast Ultrasound Image Classification

Hou, Dennis; Hou, Raymond; Hou, Janpu

doi:10.1109/ccwc47524.2020.9031146

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…Therefore, the accuracy level obtained by the proposed method using the same Database-I about 98.25% with an F1 score of 0.982 is significantly better. In another work, Hou et al [21] used the Database-II and reported an accuracy of 94.8%. Shin et al [22] reported an accuracy of 84.5% using the same Database-II combined with other databases.…”

Section: Discussionmentioning

confidence: 99%

“…Shivabalan et al [20] used a simple neural network that is cheap and easy to use and gained a satisfactory result in a small online dataset. Hou et al [21] proposed an on-device AI pre-trained neural network model which can train the CNN classifier on a portable device without a cloud-based server. Shin et al [22], in their work, illustrated a neural network with faster R-CNN and ResNet-101.…”

Section: Introductionmentioning

confidence: 99%

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RiIG Modeled WCP Image-Based CNN Architecture and Feature-Based Approach in Breast Tumor Classification from B-Mode Ultrasound

et al. 2021

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This study presents two new approaches based on Weighted Contourlet Parametric (WCP) images for the classification of breast tumors from B-mode ultrasound images. The Rician Inverse Gaussian (RiIG) distribution is considered for modeling the statistics of ultrasound images in the Contourlet transform domain. The WCP images are obtained by weighting the RiIG modeled Contourlet sub-band coefficient images. In the feature-based approach, various geometrical, statistical, and texture features are shown to have low ANOVA p-value, thus indicating a good capacity for class discrimination. Using three publicly available datasets (Mendeley, UDIAT, and BUSI), it is shown that the classical feature-based approach can yield more than 97% accuracy across the datasets for breast tumor classification using WCP images while the custom-made convolutional neural network (CNN) can deliver more than 98% accuracy, sensitivity, specificity, NPV, and PPV values utilizing the same WCP images. Both methods provide superior classification performance, better than those of several existing techniques on the same datasets.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

RiIG Modeled WCP Image-Based CNN Architecture and Feature-Based Approach in Breast Tumor Classification from B-Mode Ultrasound

et al. 2021

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“…Much of the literature developing and testing AI algorithms for breast lesion detection has focused on post-hoc analysis of images from clinical breast ultrasound devices, implicitly assuming that the exam has already been performed and representative views of the breast/lesion were captured by the sonographer. A limited number of studies have explored either AI-informed portable breast ultrasound [8][9][10] or video-based detection of lesions in breast ultrasound imaging [11][12][13]. Few studies have reported on evaluation or visualization timing for AI integrated with POCUS for real-time detection.…”

Section: Introductionmentioning

confidence: 99%

Assessing the feasibility of AI-enhanced portable ultrasound for improved early detection of breast cancer in remote areas

Zemi,

Bunnell,

Valdez

et al. 2024

17th International Workshop on Breast Imaging (IWBI 2024)

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The objective of our study was to explore the feasibility of integrating artificial intelligence (AI) algorithms for breast cancer detection into a portable, point-of-care ultrasound device (POCUS). This proof-of-concept implementation is to demonstrate the platform for integrating AI algorithms into a POCUS device to achieve a performance benchmark of at least 15 frames/second. Our methodology involved the application of five AI models (FasterRCNN+MobileNetV3, FasterRCNN+ResNet50, RetinaNet+ResNet50, SSD300+VGG16, and SSDLite320+MobileNetV3), pretrained on public datasets of natural images, fine-tuned using a dataset of gelatin-based breast phantom images with both anechoic and hyperechoic lesions, mimicking real tissue characteristics. We created various gelatin-based ultrasound phantoms containing ten simulated lesions, ranging from 4-20 mm in size. Our experimental setup used the Clarius L15 scanning probe, which was connected via Wi-Fi to both a tablet and a laptop, forming the core of our development platform. The phantom data was divided into training, validation, and held-out testing sets on a per-video basis. We executed 200 timing trials for each finetuned AI model, streaming scanning video from the ultrasound probe in real-time. SSDLite320+MobileNetV3 emerged as a standout, showing a mean frame-to-frame timing of 0.068 seconds (SD=0.005), which is approximately 14.71 FPS, closely followed by FasterRCNN+MobileNetV3, with a mean timing of 0.123 seconds (SD=0.016), or about 8.13 FPS. Both models show acceptable performance in lesion localization. Compared to our goal of 15 frames/second, only the SSDLite320+MobileNetV3 architecture performed with sufficient evaluation speed to be used in real-time. Our findings show the necessity of using AI architectures designed for edge devices for real-time use, as well as the potential need for hardware acceleration to encode AI models for use in POCUS.

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Parametric Image-based Breast Tumor Classification Using Convolutional Neural Network in the Contourlet Transform Domain

Kabir

Shihavuddin

Tanveer

et al. 2020

2020 11th International Conference on Electrical and Computer Engineering (ICECE)

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On-device Training for Breast Ultrasound Image Classification

Cited by 8 publications

References 15 publications

RiIG Modeled WCP Image-Based CNN Architecture and Feature-Based Approach in Breast Tumor Classification from B-Mode Ultrasound

RiIG Modeled WCP Image-Based CNN Architecture and Feature-Based Approach in Breast Tumor Classification from B-Mode Ultrasound

Assessing the feasibility of AI-enhanced portable ultrasound for improved early detection of breast cancer in remote areas

Parametric Image-based Breast Tumor Classification Using Convolutional Neural Network in the Contourlet Transform Domain

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