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Found 5 papers, 2 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.
Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity
Rationalizing which parts of a molecule drive the predictions of a molecular graph convolutional neural network (GCNN) can be difficult.
Gini in a Bottleneck: Sparse Molecular Representations for Graph Convolutional Neural Networks
Due to the nature of deep learning approaches, it is inherently difficult to understand which aspects of a molecular graph drive the predictions of the network.
Learning Continuous and Data-Driven Molecular Descriptors by Translating Equivalent Chemical Representations
In this work, we propose to exploit the powerful ability of deep neural networks to learn a feature representation from low-level encodings of a huge corpus of chemical structures.
