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Found 21 papers, 2 papers with code
Structured Model Pruning for Efficient Inference in Computational Pathology
In this work, we demonstrate that model pruning, as a model compression technique, can effectively reduce inference cost for computational and digital pathology based analysis with a negligible loss of analysis performance.
Comments on 'Fast and scalable search of whole-slide images via self-supervised deep learning'
Chen et al. [Chen2022] recently published the article 'Fast and scalable search of whole-slide images via self-supervised deep learning' in Nature Biomedical Engineering.
Learning Binary and Sparse Permutation-Invariant Representations for Fast and Memory Efficient Whole Slide Image Search
Learning suitable Whole slide images (WSIs) representations for efficient retrieval systems is a non-trivial task.
Decentralized Federated Learning through Proxy Model Sharing
Institutions in highly regulated domains such as finance and healthcare often have restrictive rules around data sharing.
Pay Attention with Focus: A Novel Learning Scheme for Classification of Whole Slide Images
The feature extractor model is fine-tuned using hierarchical target labels of WSIs, i. e., anatomic site and primary diagnosis.
Colored Kimia Path24 Dataset: Configurations and Benchmarks with Deep Embeddings
The Kimia Path24 dataset has been introduced as a classification and retrieval dataset for digital pathology.
Forming Local Intersections of Projections for Classifying and Searching Histopathology Images
In this paper, we propose a novel image descriptor called Forming Local Intersections of Projections (FLIP) and its multi-resolution version (mFLIP) for representing histopathology images.
Recognizing Magnification Levels in Microscopic Snapshots
In this paper, we extract deep features of the images available on TCGA dataset with known magnification to train a classifier for magnification recognition.
Representation Learning of Histopathology Images using Graph Neural Networks
Representation learning for Whole Slide Images (WSIs) is pivotal in developing image-based systems to achieve higher precision in diagnostic pathology.
Pan-Cancer Diagnostic Consensus Through Searching Archival Histopathology Images Using Artificial Intelligence
The emergence of digital pathology has opened new horizons for histopathology and cytology.
Learning Permutation Invariant Representations using Memory Networks
Many real-world tasks such as classification of digital histopathology images and 3D object detection involve learning from a set of instances.
Subtractive Perceptrons for Learning Images: A Preliminary Report
In recent years, artificial neural networks have achieved tremendous success for many vision-based tasks.
Projectron -- A Shallow and Interpretable Network for Classifying Medical Images
This paper introduces the `Projectron' as a new neural network architecture that uses Radon projections to both classify and represent medical images.
Automatic Classification of Pathology Reports using TF-IDF Features
The results are encouraging in demonstrating the potential of machine learning methods for classification and encoding of pathology reports.
Comparing LBP, HOG and Deep Features for Classification of Histopathology Images
In the present study, comparison of three classification models is conducted using features extracted using local binary patterns, the histogram of gradients, and a pre-trained deep network.
Convolutional Neural Networks for Histopathology Image Classification: Training vs. Using Pre-Trained Networks
We explore the problem of classification within a medical image data-set based on a feature vector extracted from the deepest layer of pre-trained Convolution Neural Networks.
Skin Lesion Segmentation: U-Nets versus Clustering
The first approach uses U-Nets and introduces a histogram equalization based preprocessing step.
Learning Autoencoded Radon Projections
Autoencoders have been recently used for encoding medical images.
A Comparative Study of CNN, BoVW and LBP for Classification of Histopathological Images
This paper is a comparative study describing the potential of using local binary patterns (LBP), deep features and the bag-of-visual words (BoVW) scheme for the classification of histopathological images.
Classification and Retrieval of Digital Pathology Scans: A New Dataset
In this paper, we introduce a new dataset, \textbf{Kimia Path24}, for image classification and retrieval in digital pathology.
Learning Opposites Using Neural Networks
In this paper, we introduce an approach to learn type-II opposites from the given inputs and their outputs using the artificial neural networks (ANNs).
