medical-5

Histopathological Image Classification

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in histopathological-image-classification-5

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Use these libraries to find histopathological-image-classification-5 models and implementations

Datasets

Chaoyang

DiagSet

BCNB

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Most implemented papers

Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis

A. Rakhlin, Alexandr A Kalinin, V. Iglovikov, Alexey A. Shvets•Thu Feb 01 2018

This work develops the computational approach based on deep convolution neural networks for breast cancer histology image classification that outperforms other common methods in automated histopathological image classification.

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

Seung Wook Kim, C. Srinidhi, Fu-Der Chen, Anne L. Martel•Sat Feb 06 2021

This work leverages both task-agnostic and task-specific unlabeled data based on two novel strategies based on 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.

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Regression Concept Vectors for Bidirectional Explanations in Histopathology

V. Andrearczyk, Mara Graziani, H. Müller•Wed Sep 19 2018

This work proposes a methodology to exploit continuous concept measures as Regression Concept Vectors (RCVs) in the activation space of a layer to exploit network sensitivity to increasing values of a given concept measure.

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Fisher Discriminant Triplet and Contrastive Losses for Training Siamese Networks

Mark Crowley, H. Tizhoosh, Benyamin Ghojogh, Milad Sikaroudi, Sobhan Shafiei, F. Karray•Sat Apr 04 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 and show the effectiveness of the proposed loss functions.

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Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study

Mark Crowley, H. Tizhoosh, Benyamin Ghojogh, Milad Sikaroudi, Sobhan Shafiei, Amir Safarpoor•Sat May 09 2020

This work explored the performance of a deep neural network and triplet loss in the area of representation learning, investigated the notion of similarity and dissimilarity in pathology whole-slide images and compared different setups from unsupervised and semi-supervised to supervised learning.

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Offline versus Online Triplet Mining based on Extreme Distances of Histopathology Patches

Mark Crowley, H. Tizhoosh, Benyamin Ghojogh, Milad Sikaroudi, F. Karray, Amir Safarpoor•Fri Jul 03 2020

It is found that offline and online mining approaches have comparable performances for a specific architecture, such as ResNet-18 in this study.

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Batch-Incremental Triplet Sampling for Training Triplet Networks Using Bayesian Updating Theorem

Mark Crowley, H. Tizhoosh, Benyamin Ghojogh, Milad Sikaroudi, F. Karray•Thu Jul 09 2020

Experimental results on two public datasets, namely MNIST and histopathology colorectal cancer, substantiate the effectiveness of the proposed triplet mining method with Bayesian updating and conjugate priors.

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HATNet: An End-to-End Holistic Attention Network for Diagnosis of Breast Biopsy Images

Hannaneh Hajishirzi, Ximing Lu, L. Shapiro, Sachin Mehta, D. Weaver, J. Elmore•Fri Jul 24 2020

This paper introduces a novel attention-based network, the Holistic ATtention Network (HATNet), which outperforms the previous best network Y-Net and uses self-attention to encode global information, allowing it to learn representations from clinically relevant tissue structures without any explicit supervision.

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Multiple Instance Learning with Center Embeddings for Histopathology Classification

Philip Chikontwe, Meejeong Kim, S. Nam, H. Go, Sang Hyun Park•Sat Oct 03 2020

This work proposes a MIL-based method to jointly learn both instance- and bag-level embeddings in a single framework that can accurately predict instance labels and leverages robust hierarchical pooling of features to obtain bag- level features without sacrificing accuracy.

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C-Net: A Reliable Convolutional Neural Network for Biomedical Image Classification

H. Barzekar, Zeyun Yu•Thu Oct 29 2020

A novel convolutional neural network architecture composed of a Concatenation of multiple Networks, called C-Net, to classify biomedical images, which outperforms all other models on the individual metrics for both datasets and achieves zero misclassification.

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