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   Articles with "class activation" as a keyword



Automatic diagnosis of ureteral stone and degree of hydronephrosis with proposed convolutional neural network, RelieF, and gradient‐weighted class activation mapping based deep hybrid model

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recommendations! Published in 2023 at "International Journal of Imaging Systems and Technology"

DOI: 10.1002/ima.22847

Abstract: Urinary system stone disease is a common disease group all over the world. Ureteral stones constitute 20% of all urinary system stones. Ureteral stones are important because they can cause hydronephrosis and related renal parenchymal… read more here.

Keywords: class activation; gradient weighted; hybrid model; hydronephrosis ... See more keywords

Category boundary re-decision by component labels to improve generation of class activation map

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recommendations! Published in 2022 at "Neurocomputing"

DOI: 10.1016/j.neucom.2021.10.072

Abstract: Abstract Class Activation Maps (CAMs) can visualize the contribution of each pixel to a certain category and thus, can localize objects. However, in existing generation methods, CAMs are only highly responsive to small local features… read more here.

Keywords: component; generation; level; category boundary ... See more keywords
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APPLICABILITY OF NOVEL, CLASS ACTIVATION MAPS (CAM) IN THE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE-GUIDED, SINGLE AND 12-LEAD ECG TO DETECT ST-ELEVATION MYOCARDIAL INFARCTION

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recommendations! Published in 2020 at "Journal of the American College of Cardiology"

DOI: 10.1016/s0735-1097(20)34101-2

Abstract: Convolutional Neural Networks (CNN) are seamlessly integrated into many fields, including image recognition. CAM are able to recognize specific regions of images for their classification. We applied CAM and discovered discriminative segments for the detection… read more here.

Keywords: applicability novel; guided single; class activation; activation maps ... See more keywords

Truncated and integrated class activation maps for weakly supervised defect detection

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recommendations! Published in 2025 at "Measurement Science and Technology"

DOI: 10.1088/1361-6501/adcf3d

Abstract: Deep learning is now widely used for detecting surface defects, which is crucial for automated quality control in industries. However, getting lots of accurate labeled data is tough, and this slows down the progress of… read more here.

Keywords: detection; defect detection; weakly supervised; activation maps ... See more keywords

G2Grad-CAMRL: An Object Detection and Interpretation Model Based on Gradient-Weighted Class Activation Mapping and Reinforcement Learning in Remote Sensing Images

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recommendations! Published in 2023 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"

DOI: 10.1109/jstars.2023.3241405

Abstract: Remote sensing images (RSIs) contain important information, such as airports, ports, and ships. By extracting RSI features and learning the mapping relationship between image features and text semantic features, the interpretation and description of RSI… read more here.

Keywords: network; remote sensing; reinforcement learning; class activation ... See more keywords

Artificial Intelligence Class Activation Mapping of Bone Age.

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recommendations! Published in 2022 at "Radiology"

DOI: 10.1148/radiol.211790

Abstract: Online supplemental material is available for this article. read more here.

Keywords: bone age; class activation; intelligence class; artificial intelligence ... See more keywords

Explainability of three-dimensional convolutional neural networks for functional magnetic resonance imaging of Alzheimer’s disease classification based on gradient-weighted class activation mapping

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recommendations! Published in 2024 at "PLOS ONE"

DOI: 10.1371/journal.pone.0303278

Abstract: Currently, numerous studies focus on employing fMRI-based deep neural networks to diagnose neurological disorders such as Alzheimer’s Disease (AD), yet only a handful have provided results regarding explainability. We address this gap by applying several… read more here.

Keywords: alzheimer disease; weighted class; explainability; gradient weighted ... See more keywords

Lung Cancer Classification Using Squeeze and Excitation Convolutional Neural Networks with Grad Cam++ Class Activation Function

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recommendations! Published in 2021 at "Traitement du Signal"

DOI: 10.18280/ts.380421

Abstract: The leading cause of cancer-related death globally has been identified as lung cancer. Early lung nodule detection is critical for lung cancer therapy and patient survival. The Gard Cam++ Class Activation Function is used with… read more here.

Keywords: classification; class activation; cam class; lung cancer ... See more keywords

UnionCAM: enhancing CNN interpretability through denoising, weighted fusion, and selective high-quality class activation mapping

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recommendations! Published in 2024 at "Frontiers in Neurorobotics"

DOI: 10.3389/fnbot.2024.1490198

Abstract: Deep convolutional neural networks (CNNs) have achieved remarkable success in various computer vision tasks. However, the lack of interpretability in these models has raised concerns and hindered their widespread adoption in critical domains. Generating activation… read more here.

Keywords: fusion; interpretability; activation maps; activation ... See more keywords

Multimodal Explainability Using Class Activation Maps and Canonical Correlation for MI-EEG Deep Learning Classification

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recommendations! Published in 2024 at "Applied Sciences"

DOI: 10.3390/app142311208

Abstract: Brain–computer interfaces (BCIs) are essential in advancing medical diagnosis and treatment by providing non-invasive tools to assess neurological states. Among these, motor imagery (MI), in which patients mentally simulate motor tasks without physical movement, has… read more here.

Keywords: deep learning; classification; canonical correlation; eeg ... See more keywords

Class Activation Map Guided Backpropagation for Discriminative Explanations

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recommendations! Published in 2025 at "Applied Sciences"

DOI: 10.3390/app15010379

Abstract: The interpretability of neural networks has garnered significant attention. In the domain of computer vision, gradient-based feature attribution techniques like RectGrad have been proposed to utilize saliency maps to demonstrate feature contributions to predictions. Despite… read more here.

Keywords: activation map; class; backpropagation; map guided ... See more keywords