6 research outputs found
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
Privacy Meets Explainability: A Comprehensive Impact Benchmark
Since the mid-10s, the era of Deep Learning (DL) has continued to this day,
bringing forth new superlatives and innovations each year. Nevertheless, the
speed with which these innovations translate into real applications lags behind
this fast pace. Safety-critical applications, in particular, underlie strict
regulatory and ethical requirements which need to be taken care of and are
still active areas of debate. eXplainable AI (XAI) and privacy-preserving
machine learning (PPML) are both crucial research fields, aiming at mitigating
some of the drawbacks of prevailing data-hungry black-box models in DL. Despite
brisk research activity in the respective fields, no attention has yet been
paid to their interaction. This work is the first to investigate the impact of
private learning techniques on generated explanations for DL-based models. In
an extensive experimental analysis covering various image and time series
datasets from multiple domains, as well as varying privacy techniques, XAI
methods, and model architectures, the effects of private training on generated
explanations are studied. The findings suggest non-negligible changes in
explanations through the introduction of privacy. Apart from reporting
individual effects of PPML on XAI, the paper gives clear recommendations for
the choice of techniques in real applications. By unveiling the
interdependencies of these pivotal technologies, this work is a first step
towards overcoming the remaining hurdles for practically applicable AI in
safety-critical domains.Comment: Under Submissio
Co-Design of a Trustworthy AI System in Healthcare: Deep Learning Based Skin Lesion Classifier
This paper documents how an ethically aligned co-design methodology ensures trustworthiness in the early design phase of an artificial intelligence (AI) system component for healthcare. The system explains decisions made by deep learning networks analyzing images of skin lesions. The co-design of trustworthy AI developed here used a holistic approach rather than a static ethical checklist and required a multidisciplinary team of experts working with the AI designers and their managers. Ethical, legal, and technical issues potentially arising from the future use of the AI system were investigated. This paper is a first report on co-designing in the early design phase. Our results can also serve as guidance for other early-phase AI-similar tool developments.</jats:p
Revisiting the Shape-Bias of Deep Learning for Dermoscopic Skin Lesion Classification
It is generally believed that the human visual system is biased towards the
recognition of shapes rather than textures. This assumption has led to a
growing body of work aiming to align deep models' decision-making processes
with the fundamental properties of human vision. The reliance on shape features
is primarily expected to improve the robustness of these models under covariate
shift. In this paper, we revisit the significance of shape-biases for the
classification of skin lesion images. Our analysis shows that different skin
lesion datasets exhibit varying biases towards individual image features.
Interestingly, despite deep feature extractors being inclined towards learning
entangled features for skin lesion classification, individual features can
still be decoded from this entangled representation. This indicates that these
features are still represented in the learnt embedding spaces of the models,
but not used for classification. In addition, the spectral analysis of
different datasets shows that in contrast to common visual recognition,
dermoscopic skin lesion classification, by nature, is reliant on complex
feature combinations beyond shape-bias. As a natural consequence, shifting away
from the prevalent desire of shape-biasing models can even improve skin lesion
classifiers in some cases.Comment: Submitted preprint accepted for MIUA 202