REVIEW 2 cited by
Patchnet: Interpretable Neural Networks for Image Classification
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Understanding how a complex machine learning model makes a classification decision is essential for its acceptance in sensitive areas such as health care. Towards this end, we present PatchNet, a method that provides the features indicative of each class in an image using a tradeoff between restricting global image context and classification error. We mathematically analyze this tradeoff, demonstrate Patchnet's ability to construct sharp visual heatmap representations of the learned features, and quantitatively compare these features with features selected by domain experts by applying PatchNet to the classification of benign/malignant skin lesions from the ISBI-ISIC 2017 melanoma classification challenge.
Forward citations
Cited by 2 Pith papers
-
An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification
A Grad-CAM-based methodology with similarity metrics reveals that data augmentation techniques affect learned attention patterns in distinct clusters, but individual differences are small.
-
Hybrid Interpretable Deep Learning Framework for Skin Cancer Diagnosis: Integrating Radial Basis Function Networks with Explainable AI
A prototype-based RBF classifier built on CNN segment embeddings reports 83.02% on ISIC 2016 and 76.15% on ISIC 2017, with explanations traced to cluster prototypes, but the evaluation is compromised by an undefined s...
Discussion (0). Continue with ORCID to comment.