29,216 research outputs found
Characterization of pitting corrosion of stainless steel using artificial neural networks
In this work, different classification models were proposed to predict the pitting corrosion status of AISI 316L stainless steel according to the environmental conditions and the breakdown potential values. In order to study the pitting corrosion status of this material, polarization tests were undertaken in different environmental conditions: varying chloride ion concentration, pH and temperature. Two different techniques were presented: k nearest neighbor (KNN) and Artificial Neural Networks (ANNs). The parameters for the classifiers were set based on a compromise between recall and precision using bootstrap as validation technique. The ROC space was presented to compare the classification performance of the different models. In this frame, Bayesian regularized neural network model proved to be the most promising technique to determine the pitting corrosion status of 316L stainless steel without resorting to optical metallographic studies
On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors
Deep learning based medical image classifiers have shown remarkable prowess
in various application areas like ophthalmology, dermatology, pathology, and
radiology. However, the acceptance of these Computer-Aided Diagnosis (CAD)
systems in real clinical setups is severely limited primarily because their
decision-making process remains largely obscure. This work aims at elucidating
a deep learning based medical image classifier by verifying that the model
learns and utilizes similar disease-related concepts as described and employed
by dermatologists. We used a well-trained and high performing neural network
developed by REasoning for COmplex Data (RECOD) Lab for classification of three
skin tumours, i.e. Melanocytic Naevi, Melanoma and Seborrheic Keratosis and
performed a detailed analysis on its latent space. Two well established and
publicly available skin disease datasets, PH2 and derm7pt, are used for
experimentation. Human understandable concepts are mapped to RECOD image
classification model with the help of Concept Activation Vectors (CAVs),
introducing a novel training and significance testing paradigm for CAVs. Our
results on an independent evaluation set clearly shows that the classifier
learns and encodes human understandable concepts in its latent representation.
Additionally, TCAV scores (Testing with CAVs) suggest that the neural network
indeed makes use of disease-related concepts in the correct way when making
predictions. We anticipate that this work can not only increase confidence of
medical practitioners on CAD but also serve as a stepping stone for further
development of CAV-based neural network interpretation methods.Comment: Accepted for the IEEE International Joint Conference on Neural
Networks (IJCNN) 202
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