Histopathological Image Classification

14 papers with code • 0 benchmarks • 3 datasets

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Most implemented papers

Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis

alexander-rakhlin/ICIAR2018 2 Feb 2018

In this work, we develop the computational approach based on deep convolution neural networks for breast cancer histology image classification.

Histopathological Image Classification using Discriminative Feature-oriented Dictionary Learning

tiepvupsu/DICTOL 16 Jun 2015

In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structures.

Self-supervised driven consistency training for annotation efficient histopathology image analysis

srinidhiPY/SSL_CR_Histo 7 Feb 2021

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

medgift/iMIMIC-RCVs 9 Apr 2019

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 5 Apr 2020

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 10 May 2020

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 4 Jul 2020

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 10 Jul 2020

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 25 Jul 2020

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 29 Sep 2020

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.