Histopathological Image Classification
17 papers with code • 0 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Histopathological Image Classification
Chaoyang
DiagSet
BCNB
Most implemented papers
Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis
alexander-rakhlin/ICIAR2018
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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
bghojogh/Fisher-Triplet-Contrastive-Loss
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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
bghojogh/Siamese-Network-Histopathology
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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
bghojogh/Offline-Online-Triplet-Mining
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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
bghojogh/BUT-BUNCA-Triplet-Sampling
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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
sacmehta/HATNet
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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
PhilipChicco/MICCAI2020mil
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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
Deeksha-5/C-Net_Pytorch_Implementation
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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.
