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Found 9 papers, 4 papers with code
Evaluating histopathology transfer learning with ChampKit
Histopathology remains the gold standard for diagnosis of various cancers.
Federated Learning for the Classification of Tumor Infiltrating Lymphocytes
We evaluate the performance of federated learning (FL) in developing deep learning models for analysis of digitized tissue sections.
GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging
Deep Learning (DL) has the potential to optimize machine learning in both the scientific and clinical communities.
Dataset of Segmented Nuclei in Hematoxylin and Eosin Stained Histopathology Images of 10 Cancer Types
To address this, we developed an analysis pipeline that segments nuclei in whole slide tissue images from multiple cancer types with a quality control process.
Unsupervised Histopathology Image Synthesis
We propose a unified pipeline that: a) generates a set of initial synthetic histopathology images with paired information about the nuclei such as segmentation masks; b) refines the initial synthetic images through a Generative Adversarial Network (GAN) to reference styles; c) trains a task-specific CNN and boosts the performance of the task-specific CNN with on-the-fly generated adversarial examples.
Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images
In this work, we propose a sparse Convolutional Autoencoder (CAE) for fully unsupervised, simultaneous nucleus detection and feature extraction in histopathology tissue images.
Center-Focusing Multi-task CNN with Injected Features for Classification of Glioma Nuclear Images
Classifying the various shapes and attributes of a glioma cell nucleus is crucial for diagnosis and understanding the disease.
Neural Networks with Smooth Adaptive Activation Functions for Regression
In this paper, we propose and apply AAFs on feedforward NNs for regression tasks.
Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification
However, to recognize cancer subtypes automatically, training a CNN on gigapixel resolution Whole Slide Tissue Images (WSI) is currently computationally impossible.
