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Found 15 papers, 5 papers with code
Efficient Parameter Optimisation for Quantum Kernel Alignment: A Sub-sampling Approach in Variational Training
Quantum machine learning with quantum kernels for classification problems is a growing area of research.
SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathology
Introducing interpretability and reasoning into Multiple Instance Learning (MIL) methods for Whole Slide Image (WSI) analysis is challenging, given the complexity of gigapixel slides.
Multi-scale Feature Alignment for Continual Learning of Unlabeled Domains
Methods for unsupervised domain adaptation (UDA) help to improve the performance of deep neural networks on unseen domains without any labeled data.
Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer
These pseudo labels are then used to train a node classification head for WSI segmentation.
Generative appearance replay for continual unsupervised domain adaptation
In contrast to single-step unsupervised domain adaptation (UDA), continual adaptation to a sequence of domains enables leveraging and consolidation of information from multiple domains.
Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images
Multiple Instance Learning (MIL) methods have become increasingly popular for classifying giga-pixel sized Whole-Slide Images (WSIs) in digital pathology.
BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images
Each WSI, and respective ROIs, are annotated by the consensus of three board-certified pathologists into different lesion categories.
HistoCartography: A Toolkit for Graph Analytics in Digital Pathology
Advances in entity-graph based analysis of histopathology images have brought in a new paradigm to describe tissue composition, and learn the tissue structure-to-function relationship.
Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphs
Thus, weakly-supervised semantic segmentation techniques are proposed to utilize weak supervision that is cheaper and quicker to acquire.
Hierarchical Graph Representations in Digital Pathology
We propose a novel multi-level hierarchical entity-graph representation of tissue specimens to model hierarchical compositions that encode histological entities as well as their intra- and inter-entity level interactions.
Quantifying Explainers of Graph Neural Networks in Computational Pathology
However, popular deep learning methods and explainability techniques (explainers) based on pixel-wise processing disregard biological entities' notion, thus complicating comprehension by pathologists.
HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification
Further, a hierarchical graph neural network (HACT-Net) is proposed to efficiently map the HACT representations to histopathological breast cancer subtypes.
General Classification
Histopathological Image Classification
+1
Towards Explainable Graph Representations in Digital Pathology
Explainability of machine learning (ML) techniques in digital pathology (DP) is of great significance to facilitate their wide adoption in clinics.
NINEPINS: Nuclei Instance Segmentation with Point Annotations
Deep learning-based methods are gaining traction in digital pathology, with an increasing number of publications and challenges that aim at easing the work of systematically and exhaustively analyzing tissue slides.
Mitosis Detection Under Limited Annotation: A Joint Learning Approach
Our framework significantly improves the detection with small training data and achieves on par or superior performance compared to state-of-the-art methods for using the entire training data.
