CDPath21
* *Computational Challenges in Digital Pathology
* ALBRT: Cellular Composition Prediction in Routine Histology Images
* Deep Ordinal Focus Assessment for Whole Slide Images
* Guided Representation Learning for the Classification of Hematopoietic Cells
* H&E-adversarial network: a convolutional neural network to learn stain-invariant features through Hematoxylin & Eosin regression
* Improving Self-supervised Learning with Hardness-aware Dynamic Curriculum Learning: An Application to Digital Pathology
* investigation of attention mechanisms in histopathology whole-slide-image analysis for regression objectives, An
* Iterative Cross-Scanner Registration for Whole Slide Images
* Joint Semi-supervised and Active Learning for Segmentation of Gigapixel Pathology Images with Cost-Effective Labeling
* Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classification
* Multi-Prototype Few-shot Learning in Histopathology
* Pathology Deep Learning System Capable of Triage of Melanoma Specimens Utilizing Dermatopathologist Consensus as Ground Truth *, A
* Probeable DARTS with Application to Computational Pathology
* QuadTree Image Representation for Computational Pathology, A
* Real-Time Cell Counting in Unlabeled Microscopy Images
* Robust Interactive Semantic Segmentation of Pathology Images with Minimal User Input
* Self-Supervised Representation Learning using Visual Field Expansion on Digital Pathology
* Simultaneous Nuclear Instance and Layer Segmentation in Oral Epithelial Dysplasia
18 for CDPath21