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Found 6 papers, 0 papers with code
Breast Histopathology Image Retrieval by Attention-based Adversarially Regularized Variational Graph Autoencoder with Contrastive Learning-Based Feature Extraction
Breast cancer is a significant global health concern, particularly for women.
FORML: A Riemannian Hessian-free Method for Meta-learning with Orthogonality Constraint
However, performing the optimization in the Riemannian space, where the parameters and meta-parameters are located on Riemannian manifolds is computationally intensive.
Multi-modal reward for visual relationships-based image captioning
To take advantage of visual relationships in caption generation, this paper proposes a deep neural network architecture for image captioning based on fusing the visual relationships information extracted from an image's scene graph with the spatial feature maps of the image.
Deep fusion of gray level co-occurrence matrices for lung nodule classification
The proposed methods are trained and assessed through the LIDC-IDRI dataset, where 94. 4%, 91. 6%, and 95. 8% Accuracy, sensitivity, and specificity are obtained, respectively for 2D-GLCM fusion and 97. 33%, 96%, and 98%, accuracy, sensitivity, and specificity, respectively, for 2. 5D-GLCM fusion.
Weighted Least Squares Twin Support Vector Machine with Fuzzy Rough Set Theory for Imbalanced Data Classification
In this work, we propose an approach that efficiently used fuzzy rough set theory in weighted least squares twin support vector machine called FRLSTSVM for classification of imbalanced data.
PCB Defect Detection Using Denoising Convolutional Autoencoders
We trained our model with defective PCBs and forced it to repair the defective parts.
