About

Benchmarks

No evaluation results yet. Help compare methods by submit evaluation metrics.

Greatest papers with code

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

7 Feb 2021srinidhiPY/SSL_CR_Histo

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.

BREAST TUMOUR CLASSIFICATION CLASSIFICATION OF BREAST CANCER HISTOLOGY IMAGES HISTOPATHOLOGICAL IMAGE CLASSIFICATION SELF-SUPERVISED LEARNING

Fisher Discriminant Triplet and Contrastive Losses for Training Siamese Networks

5 Apr 2020bghojogh/Fisher-Triplet-Contrastive-Loss

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.

CLASSIFICATION OF BREAST CANCER HISTOLOGY IMAGES DIMENSIONALITY REDUCTION DOMAIN GENERALIZATION HISTOPATHOLOGICAL IMAGE CLASSIFICATION METRIC LEARNING