Search Results for author:
Found 3 papers, 2 papers with code
Consistency driven Sequential Transformers Attention Model for Partially Observable Scenes
Most hard attention models initially observe a complete scene to locate and sense informative glimpses, and predict class-label of a scene based on glimpses.
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.
Histopathological Image Classification
Representation Learning
+1
Deep neural network models for computational histopathology: A survey
Histopathological images contain rich phenotypic information that can be used to monitor underlying mechanisms contributing to diseases progression and patient survival outcomes.
