Histopathological Image Classification

Benchmarks archive 2025-07-28

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Libraries

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Datasets archive 2025-07-28

3 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

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Most implemented papers archive 2025-07-28

23 shown of 23 papers with code (40 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis
  2 Feb 2018 3 repositories listed Syntology ran 4 of 4 samples · 0 unverified · 3 pointer-only (licence)
  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
  7 Feb 2021 2 repositories listed
  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…
• FMDNN: A Fuzzy-guided Multi-granular Deep Neural Network for Histopathological Image Classification
  22 Jul 2024 1 repository listed
  Inspired by the multi-granular diagnostic approach of pathologists, we perform feature extraction on cell structures at coarse, medium, and fine granularity, enabling the model to fully harness the information in…
• Histopathological Image Classification with Cell Morphology Aware Deep Neural Networks
  11 Jul 2024 1 repository listed
  To deal with this problem, we propose a novel DeepCMorph model pre-trained to learn cell morphology and identify a large number of different cancer types.
• CLASS-M: Adaptive stain separation-based contrastive learning with pseudo-labeling for histopathological image classification
  12 Dec 2023 1 repository listed
  On the other hand, acquiring extensive datasets with localized labels for training is not feasible.
• Automatic Report Generation for Histopathology images using pre-trained Vision Transformers and BERT
  3 Dec 2023 1 repository listed
  Deep learning for histopathology has been successfully used for disease classification, image segmentation and more.
• SHISRCNet: Super-resolution And Classification Network For Low-resolution Breast Cancer Histopathology Image
  25 Jun 2023 1 repository listed
  CF module extracts and fuses the multi-scale features of SR images for classification.
• Slideflow: Deep Learning for Digital Histopathology with Real-Time Whole-Slide Visualization
  9 Apr 2023 1 repository listed
  Deep learning methods have emerged as powerful tools for analyzing histopathological images, but current methods are often specialized for specific domains and software environments, and few open-source options exist…
• Histopathological Image Classification based on Self-Supervised Vision Transformer and Weak Labels
  17 Oct 2022 1 repository listed
  Here, we propose Self-ViT-MIL, a novel approach for classifying and localizing cancerous areas based on slide-level annotations, eliminating the need for pixel-wise annotated training data.
• DLTTA: Dynamic Learning Rate for Test-time Adaptation on Cross-domain Medical Images
  27 May 2022 1 repository listed
  Based on this estimated discrepancy, a dynamic learning rate adjustment strategy is then developed to achieve a suitable degree of adaptation for each test sample.
• ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification
  15 Feb 2022 1 repository listed
  We further introduce a novel mixing data-augmentation, namely ScoreMix, by leveraging the image's semantic distribution to guide the data mixing and produce coherent sample-label pairs.
• Magnification-independent Histopathological Image Classification with Similarity-based Multi-scale Embeddings
  2 Jul 2021 1 repository listed
  Experimental results show that the SMSE improves the performance for histopathological image classification tasks for both breast and liver cancers by a large margin compared to previous methods.
• DiagSet: a dataset for prostate cancer histopathological image classification
  9 May 2021 1 repository listed
  Cancer diseases constitute one of the most significant societal challenges.
• UniToPatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading
  25 Jan 2021 1 repository listed
  Histopathological characterization of colorectal polyps allows to tailor patients' management and follow up with the ultimate aim of avoiding or promptly detecting an invasive carcinoma.
• Magnification Generalization for Histopathology Image Embedding
  18 Jan 2021 1 repository listed
  However, a useful task in histopathology embedding is to train an embedding space regardless of the magnification level.
• C-Net: A Reliable Convolutional Neural Network for Biomedical Image Classification
  30 Oct 2020 1 repository listed
  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.
• Multiple Instance Learning with Center Embeddings for Histopathology Classification
  29 Sep 2020 1 repository listed
  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.
• HATNet: An End-to-End Holistic Attention Network for Diagnosis of Breast Biopsy Images
  25 Jul 2020 1 repository listed
  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.
• Batch-Incremental Triplet Sampling for Training Triplet Networks Using Bayesian Updating Theorem
  10 Jul 2020 1 repository listed
  However, sampling from stochastic distributions of data rather than sampling merely from the existing embedding instances can provide more discriminative information.
• Offline versus Online Triplet Mining based on Extreme Distances of Histopathology Patches
  4 Jul 2020 1 repository listed
  We analyze the effect of offline and online triplet mining for colorectal cancer (CRC) histopathology dataset containing 100, 000 patches.
• Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study
  10 May 2020 1 repository listed
  In this work, we explored the performance of a deep neural network and triplet loss in the area of representation learning.
• Fisher Discriminant Triplet and Contrastive Losses for Training Siamese Networks
  5 Apr 2020 1 repository listed
  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.
• Regression Concept Vectors for Bidirectional Explanations in Histopathology
  9 Apr 2019 1 repository listed
  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.

Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.