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Transfer Learning for Oral Cancer Detection using Microscopic Images
Oral cancer has more than 83 however, only 29 techniques can detect patt...
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RCCNet: An Efficient Convolutional Neural Network for Histological Routine Colon Cancer Nuclei Classification
Efficient and precise classification of histological cell nuclei is of u...
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Understanding attention in graph neural networks
We aim to better understand attention over nodes in graph neural network...
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Pre-Training on Dynamic Graph Neural Networks
The pre-training on the graph neural network model can learn the general...
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Classification of Cervical Cancer Dataset
Cervical cancer is the leading gynecological malignancy worldwide. This ...
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Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data
In this paper, we tackle the problem of relational behavior forecasting ...
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An Efficient Approximate kNN Graph Method for Diffusion on Image Retrieval
The application of the diffusion in many computer vision and artificial ...
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Efficient Colon Cancer Grading with Graph Neural Networks
Dealing with the application of grading colorectal cancer images, this work proposes a 3 step pipeline for prediction of cancer levels from a histopathology image. The overall model performs better compared to other state of the art methods on the colorectal cancer grading data set and shows excellent performance for the extended colorectal cancer grading set. The performance improvements can be attributed to two main factors: The feature selection and graph augmentation method described here are spatially aware, but overall pixel position independent. Further, the graph size in terms of nodes becomes stable with respect to the model's prediction and accuracy for sufficiently large models. The graph neural network itself consists of three convolutional blocks and linear layers, which is a rather simple design compared to other networks for this application.
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