Histopathological Image Classification
17 papers with code • 0 benchmarks • 3 datasets
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Most implemented papers
Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis
In this work, we develop the computational approach based on deep convolution neural networks for breast cancer histology image classification.
Self-supervised driven consistency training for annotation efficient histopathology image analysis
In this work, we overcome this challenge by leveraging both task-agnostic and task-specific unlabeled data based on two novel strategies: i) a self-supervised pretext task that harnesses the underlying multi-resolution contextual cues in histology whole-slide images to learn a powerful supervisory signal for unsupervised representation learning; ii) a new teacher-student semi-supervised consistency paradigm that learns to effectively transfer the pretrained representations to downstream tasks based on prediction consistency with the task-specific un-labeled data.
Regression Concept Vectors for Bidirectional Explanations in Histopathology
Explanations for deep neural network predictions in terms of domain-related concepts can be valuable in medical applications, where justifications are important for confidence in the decision-making.
Fisher Discriminant Triplet and Contrastive Losses for Training Siamese Networks
The FDT and FDC loss functions are designed based on the statistical formulation of the Fisher Discriminant Analysis (FDA), which is a linear subspace learning method.
Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study
In this work, we explored the performance of a deep neural network and triplet loss in the area of representation learning.
Offline versus Online Triplet Mining based on Extreme Distances of Histopathology Patches
We analyze the effect of offline and online triplet mining for colorectal cancer (CRC) histopathology dataset containing 100, 000 patches.
Batch-Incremental Triplet Sampling for Training Triplet Networks Using Bayesian Updating Theorem
However, sampling from stochastic distributions of data rather than sampling merely from the existing embedding instances can provide more discriminative information.
HATNet: An End-to-End Holistic Attention Network for Diagnosis of Breast Biopsy Images
HATNet extends the bag-of-words approach and uses self-attention to encode global information, allowing it to learn representations from clinically relevant tissue structures without any explicit supervision.
Multiple Instance Learning with Center Embeddings for Histopathology Classification
To address this, recent methods have considered WSI classification as a Multiple Instance Learning (MIL) problem often with a multi-stage process for learning instance and slide level features.
C-Net: A Reliable Convolutional Neural Network for Biomedical Image Classification
In this study, we propose a novel convolutional neural network (CNN) architecture composed of a Concatenation of multiple Networks, called C-Net, to classify biomedical images.