Histopathological Image Classification
21 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
Magnification Generalization for Histopathology Image Embedding
bghojogh/Histopathology-Magnification-Generalization
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However, a useful task in histopathology embedding is to train an embedding space regardless of the magnification level.
UniToPatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading
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.
DiagSet: a dataset for prostate cancer histopathological image classification
Cancer diseases constitute one of the most significant societal challenges.
Magnification-independent Histopathological Image Classification with Similarity-based Multi-scale Embeddings
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.
ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification
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.
DLTTA: Dynamic Learning Rate for Test-time Adaptation on Cross-domain Medical Images
med-air/dltta
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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.
Histopathological Image Classification based on Self-Supervised Vision Transformer and Weak Labels
gokberkgul/self-learning-transformer-mil
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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.
Slideflow: Deep Learning for Digital Histopathology with Real-Time Whole-Slide Visualization
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 for deploying models in an interactive interface.
SHISRCNet: Super-resolution And Classification Network For Low-resolution Breast Cancer Histopathology Image
CF module extracts and fuses the multi-scale features of SR images for classification.
Automatic Report Generation for Histopathology images using pre-trained Vision Transformers and BERT
ssen7/histo_cap_transformers
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Deep learning for histopathology has been successfully used for disease classification, image segmentation and more.
