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Found 9 papers, 3 papers with code
Her2 Challenge Contest: A Detailed Assessment of Automated Her2 Scoring Algorithms in Whole Slide Images of Breast Cancer Tissues
In this paper, we report on a recent automated Her2 scoring contest, held in conjunction with the annual PathSoc meeting held in Nottingham in June 2016, aimed at systematically comparing and advancing the state-of-the-art Artificial Intelligence (AI) based automated methods for Her2 scoring.
Generalized orderless pooling performs implicit salient matching
In this paper, we generalize average and bilinear pooling to "alpha-pooling", allowing for learning the pooling strategy during training.
ImageNet pre-trained models with batch normalization
Convolutional neural networks (CNN) pre-trained on ImageNet are the backbone of most state-of-the-art approaches.
Fine-grained Recognition in the Noisy Wild: Sensitivity Analysis of Convolutional Neural Networks Approaches
In this paper, we study the sensitivity of CNN outputs with respect to image transformations and noise in the area of fine-grained recognition.
Neither Quick Nor Proper -- Evaluation of QuickProp for Learning Deep Neural Networks
Neural networks and especially convolutional neural networks are of great interest in current computer vision research.
Fine-grained Recognition Datasets for Biodiversity Analysis
In the following paper, we present and discuss challenging applications for fine-grained visual classification (FGVC): biodiversity and species analysis.
Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks
Part models of object categories are essential for challenging recognition tasks, where differences in categories are subtle and only reflected in appearances of small parts of the object.
Convolutional Patch Networks with Spatial Prior for Road Detection and Urban Scene Understanding
Classifying single image patches is important in many different applications, such as road detection or scene understanding.
Part Detector Discovery in Deep Convolutional Neural Networks
Current fine-grained classification approaches often rely on a robust localization of object parts to extract localized feature representations suitable for discrimination.
