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Found 4 papers, 1 papers with code
From slides (through tiles) to pixels: an explainability framework for weakly supervised models in pre-clinical pathology
We validate quantitatively our methods by quantifying the agreements of our explanations' heatmaps with pathologists' annotations, as well as with predictions from a segmentation model trained on such annotations.
Explainable Artificial Intelligence (XAI)
whole slide images
Explaining, Evaluating and Enhancing Neural Networks' Learned Representations
Finally, we show that adopting our proposed scores as constraints during the training of a representation learning task improves the downstream performance of the model.
Unsupervised Learning of Group Invariant and Equivariant Representations
In this work, we extend group invariant and equivariant representation learning to the field of unsupervised deep learning.
Parameterized Hypercomplex Graph Neural Networks for Graph Classification
Despite recent advances in representation learning in hypercomplex (HC) space, this subject is still vastly unexplored in the context of graphs.
Ranked #15 on
Graph Property Prediction
on ogbg-molpcba
