Quality Control of Whole Slide Images using the YOLO Concept

Hemmatirad, Kimia; Babaie, Morteza; Afshari, Mehdi; Maleki, Danial; Saiadi, Mahjabin; Tizhoosh, Hamid R.

doi:10.1109/ichi54592.2022.00049

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…Our novel end-to-end MIL architecture for LN classification, LupusNet, works on raw glomerular patches, extracted using fine-tuned YOLOv4 model (Hemmatirad et al, 2023), with two key components: (a) Feature Extractor (f ) and (b) Feature Aggregator (g), jointly trained. f transforms inputs into an information-rich feature space using a ResNet-50 network pre-trained on histopathology images (Kang et al, 2023).…”

Section: Methodsmentioning

confidence: 99%

Lupus Nephritis Subtype Classification with only Slide Level labels

Sharma,

Chauhan,

Uppin

et al. 2023

Preprint

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Lupus Nephritis classification has historically relied on labor-intensive and meticulous glomerular-level labeling of renal structures in whole slide images (WSIs). However, this approach presents a formidable challenge due to its tedious and resource-intensive nature, limiting its scalability and practicality in clinical settings. In response to this challenge, our work introduces a novel methodology that utilizes only slide-level labels, eliminating the need for granular glomerular-level labeling. A comprehensive multi-stained lupus nephritis digital histopathology WSI dataset was created from the Indian population, which is the largest of its kind.LupusNet, a deep learning MIL-based model, was developed for subtype classification of LN. The results underscore its effectiveness, achieving an AUC score of 91.0%, an F1-score of 77.3%, and an accuracy of 81.1% on our dataset in distinguishing membranous and diffused classes of LN.

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Section: Methodsmentioning

confidence: 99%

Lupus Nephritis Subtype Classification with only Slide Level labels

Sharma,

Chauhan,

Uppin

et al. 2023

Preprint

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“…This is a one-stage detector abbreviated as "You Only Look Once". YOLO v7 [34,35] is an object detection method in computer vision designed to address various challenges in real-time image processing. This algorithm maintains high accuracy during the real-time processing speed of 5 to 160 frames per second (FPS) and is the latest detection technique of this era that is highly in demand for using various kinds of solutions [36,37].…”

Section: Violence Object Detection Model (Yolo V7)mentioning

confidence: 99%

Enhancing Smart City Safety and Utilizing AI Expert Systems for Violence Detection

Kumar,

Shih,

Guo

et al. 2024

Future Internet

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Violent attacks have been one of the hot issues in recent years. In the presence of closed-circuit televisions (CCTVs) in smart cities, there is an emerging challenge in apprehending criminals, leading to a need for innovative solutions. In this paper, the propose a model aimed at enhancing real-time emergency response capabilities and swiftly identifying criminals. This initiative aims to foster a safer environment and better manage criminal activity within smart cities. The proposed architecture combines an image-to-image stable diffusion model with violence detection and pose estimation approaches. The diffusion model generates synthetic data while the object detection approach uses YOLO v7 to identify violent objects like baseball bats, knives, and pistols, complemented by MediaPipe for action detection. Further, a long short-term memory (LSTM) network classifies the action attacks involving violent objects. Subsequently, an ensemble consisting of an edge device and the entire proposed model is deployed onto the edge device for real-time data testing using a dash camera. Thus, this study can handle violent attacks and send alerts in emergencies. As a result, our proposed YOLO model achieves a mean average precision (MAP) of 89.5% for violent attack detection, and the LSTM classifier model achieves an accuracy of 88.33% for violent action classification. The results highlight the model’s enhanced capability to accurately detect violent objects, particularly in effectively identifying violence through the implemented artificial intelligence system.

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“…Some studies of object detection or signal detection have various kinds of result variations, and their outcomes vary due to their different kinds of versions [22]. Every year, the YOLO version is updated with some new advances in the model architecture [23]. In a series of YOLO, introduced in 2016 and the first real-time object detection algorithm, YOLO V1 converts input images to the grid and predicts label boxes and probabilities from the grid cells.…”

Section: Introductionmentioning

confidence: 99%

“…The main lack of this version is a lower value result of mean average precision (MAP). In 2017, YOLO V2 was introduced, also known as YOLO 9000, to improve the lack of YOLO v1; this version introduced the anchor boxes and feature pyramid network (FPN) in various scales, and it improves the detection accuracy [22][23][24]. This YOLO V2 used Darknet-19 and Darknet-53 as the backbone that was used for small datasets and custom datasets in maximum count, respectively.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Vibration Monitoring System for Smart Industry Utilizing Optical Fiber Sensor Combined with Machine Learning

Kumar,

Shih,

Yao

et al. 2023

Electronics

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In this paper, we proposed and experimentally demonstrated the association of a fiber Bragg Grating (FBG) sensing system with You Only Look Once V7 (YOLO V7) to identify the vibration signal of a faulty machine. In the experiment, the YOLO V7 network architecture consists of a backbone, three detection heads (Headx3), a path aggregation network (PAN), and a feature pyramid network (FPN). The proposed architecture has an FBG sensor and the FBG interrogator employed for collecting sensing vibration signals or vibration data when degradation or fault occurs. An FBG interrogator collects vibration data independently, and then the YOLO V7 object detection algorithm is the recognition architecture of the vibration pattern of the signal. Thus, the proposed vibration recognition or detection is an assurance for detecting vibration signals that can support monitoring the machine’s health. Moreover, this research is promising for ensuring a high accuracy detection of faulty signals rate in industrial equipment monitoring and offers a robust system, resulting in remarkable accuracy with an overall model accuracy of 99.7%. The result shows that the model can identify the faulty signal more accurately and effectively detect the faulty vibration signal using the detection algorithm.

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Quality Control of Whole Slide Images using the YOLO Concept

Cited by 4 publications

References 15 publications

Lupus Nephritis Subtype Classification with only Slide Level labels

Lupus Nephritis Subtype Classification with only Slide Level labels

Enhancing Smart City Safety and Utilizing AI Expert Systems for Violence Detection

Intelligent Vibration Monitoring System for Smart Industry Utilizing Optical Fiber Sensor Combined with Machine Learning

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