The limitations of domain dependence in neural networks and data scarcity are addressed in this paper by analyzing the problem of semi-supervised medical image classification across multiple visual domains using a sin...
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ISBN:
(纸本)9783031235986;9783031235993
The limitations of domain dependence in neural networks and data scarcity are addressed in this paper by analyzing the problem of semi-supervised medical image classification across multiple visual domains using a single integrated framework. Under this premise, we learn a universal parametric family of neural networks, which share a majority of their weights across domains by learning a few adaptive domain-specific parameters. We train these universal networks on a suitable pretext task that captures a meaningful representation for image classification and further fine-tune the networks using a small fraction of training data. We perform our experiments on five medical datasets spanning breast, cervical, and colorectal cancer. Extensive experiments on architectures of domain-adaptive parameters demonstrate that our data-deficient universal model performs equivalently to a fully supervised setup, rendering a semi-supervised multi-domain setting with lower numbers of training samples for medical data extremely feasible in the real world.
The proceedings contain 39 papers. The topics discussed include: vibration region extraction method of bridge based on ground-based MIMO radar;academic paper knowledge graph, the construction and application;research ...
The proceedings contain 39 papers. The topics discussed include: vibration region extraction method of bridge based on ground-based MIMO radar;academic paper knowledge graph, the construction and application;research on test case generation method of airborne software based on NLP;3D dynamic hand gesture recognition with fused RGB and depth images;refined balanced resource allocation based on Kubernetes;visible region enhancement network for occluded pedestrian detection;a hierarchical-based frequent itemset mining method under local differential privacy;similarity-aware attention network for multimodal fake news detection;pedestrian attribute recognition based on multi-scale feature fusion over a larger receptive field and strip pooling;stowaway mining: a selfish mining against strategy;spatiotemporal attention networks for traffic demand prediction;and benefits analysis of Chinese cities on urban networks based on evolutionary games.
Faulty recognition and recovery is extremely essential component for existing sensor networks. However, current investigations concentrates on the recognition process and develop abundant advanced identification metho...
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Introduction: Early detection and treatment are key to improving the prognosis of endometrial cancer. However, conventional machine learning approaches have limited capacity to simulate the complex links between histo...
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ISBN:
(数字)9783031686177
ISBN:
(纸本)9783031686160;9783031686177
Introduction: Early detection and treatment are key to improving the prognosis of endometrial cancer. However, conventional machine learning approaches have limited capacity to simulate the complex links between histopathological images and their interpretations, making it challenging to achieve accurate results. A vision transformer-based image classification model has been proposed to assist medical professionals in detecting endometrial cancer and improving patient outcomes. Objective: This study aims to develop and evaluate a vision transformer-based model for accurately detecting histopathology images of endometrium, and compare its performance against existing fine-tuning methods such as MobilenetV2, Xception, and VGG16. Methods: A publicly accessible histopathology imaging dataset of endometrium was used to train and validate the proposed model. The performance of the model was evaluated against state-of-the-art approaches in the field. Results: The validation results showed that the proposed model attained an accuracy of 99.36%, surpassing the performance of existing fine-tuning methods and achieving the state-of-the-art performance in the widely used endometrial cancer benchmark dataset. These findings highlight the potential of vision transformer-based models in accurately detecting histopathology images of endometrium, which could lead to better patient outcomes. Conclusions: The proposed vision transformer-based model provides a highly accurate and efficient approach to detecting endometrial cancer. This study underscores the potential of this model as a valuable tool for medical professionals in the early detection and treatment of endometrial cancer, ultimately improving patient outcomes.
Lenders receive loans from investors in the lending industry with the intention of returning interest. Credit is required and helps banks decide whether to provide a loan to a person or not by forecasting the risk tha...
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Topic modeling is a popular unsupervised machine learning technique that can assist healthcare providers and policymakers in analyzing vast amounts of unstructured text data, such as patient reviews, to improve the qu...
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This paper begins with a review of existing learning methods, highlighting the limitations that hinder motivation, development of confidence, and memory retention. To address these gaps, we propose an e-learning strat...
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Due to a large number of capsule endoscopy (CE) images, it is extremely challenging to classify lesions directly from the images. Convolutional Neural Network (CNN) has been widely used in medical image processing. Ho...
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The Inter-Class Word Similarities in combination with Intra-Class Variations make it a difficult task for an OCR or any other machine learning system to recognize the handwritten characters and words with high accurac...
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