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Found 8 papers, 0 papers with code
Large-Margin Representation Learning for Texture Classification
The core of such an approach is a loss function that computes the distances between instances of interest and support vectors.
Multiscale Analysis for Improving Texture Classification
Image pyramid multiresolution representations are a useful data structure for image analysis and manipulation over a spectrum of spatial scales.
Machine Learning Methods for Histopathological Image Analysis: A Review
Histopathological images (HIs) are the gold standard for evaluating some types of tumors for cancer diagnosis.
Data Augmentation for Histopathological Images Based on Gaussian-Laplacian Pyramid Blending
Such a problem is troublesome because most of the ML algorithms attempt to optimize a loss function that does not take into account the data imbalance.
Texture CNN for Histopathological Image Classification
Biopsies are the gold standard for breast cancer diagnosis.
A Novel Orthogonal Direction Mesh Adaptive Direct Search Approach for SVM Hyperparameter Tuning
In this paper, we propose the use of a black-box optimization method called deterministic Mesh Adaptive Direct Search (MADS) algorithm with orthogonal directions (Ortho-MADS) for the selection of hyperparameters of Support Vector Machines with a Gaussian kernel.
Double Transfer Learning for Breast Cancer Histopathologic Image Classification
This work proposes a classification approach for breast cancer histopathologic images (HI) that uses transfer learning to extract features from HI using an Inception-v3 CNN pre-trained with ImageNet dataset.
Histopathologic Image Processing: A Review
In this work we present a literature review about the computing techniques to process HI, including shallow and deep methods.
