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
Latest papers
CLASS-M: Adaptive stain separation-based contrastive learning with pseudo-labeling for histopathological image classification
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
ssen7/histo_cap_transformers
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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
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
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.
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.
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.
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.
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.
DiagSet: a dataset for prostate cancer histopathological image classification
Cancer diseases constitute one of the most significant societal challenges.
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.
