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Found 10 papers, 4 papers with code
Switching Loss for Generalized Nucleus Detection in Histopathology
Remarkably, without retraining on target datasets, our pre-trained nucleus detector also outperformed existing nucleus detectors that were trained on at least some of the images from the target datasets.
Visualization for Histopathology Images using Graph Convolutional Neural Networks
With the increase in the use of deep learning for computer-aided diagnosis in medical images, the criticism of the black-box nature of the deep learning models is also on the rise.
Uncertainty Estimation in Cancer Survival Prediction
Survival models are used in various fields, such as the development of cancer treatment protocols.
Breast Cancer Histopathology Image Classification and Localization using Multiple Instance Learning
We aim to provide a better interpretation of classification results by providing localization on microscopic histopathology images.
Pixel-wise Segmentation of Right Ventricle of Heart
One of the first steps in the diagnosis of most cardiac diseases, such as pulmonary hypertension, coronary heart disease is the segmentation of ventricles from cardiac magnetic resonance (MRI) images.
Histographs: Graphs in Histopathology
Spatial arrangement of cells of various types, such as tumor infiltrating lymphocytes and the advancing edge of a tumor, are important features for detecting and characterizing cancers.
MIST: A Novel Training Strategy for Low-latencyScalable Neural Net Decoders
The enhanced decoding speed is due to the use of convolutional neural network (CNN) as opposed to recurrent neural network (RNN) used in the best known neural net based decoders.
Fast GPU-Enabled Color Normalization for Digital Pathology
Normalizing unwanted color variations due to differences in staining processes and scanner responses has been shown to aid machine learning in computational pathology.
Some New Layer Architectures for Graph CNN
While convolutional neural networks (CNNs) have recently made great strides in supervised classification of data structured on a grid (e. g. images composed of pixel grids), in several interesting datasets, the relations between features can be better represented as a general graph instead of a regular grid.
Classification of Breast Cancer Histology using Deep Learning
In this paper, we propose a deep learning-based method for classification of H&E stained breast tissue images released for BACH challenge 2018 by fine-tuning Inception-v3 convolutional neural network (CNN) proposed by Szegedy et al.
