53,460 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
Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis
Tissue characterization has long been an important component of Computer
Aided Diagnosis (CAD) systems for automatic lesion detection and further
clinical planning. Motivated by the superior performance of deep learning
methods on various computer vision problems, there has been increasing work
applying deep learning to medical image analysis. However, the development of a
robust and reliable deep learning model for computer-aided diagnosis is still
highly challenging due to the combination of the high heterogeneity in the
medical images and the relative lack of training samples. Specifically,
annotation and labeling of the medical images is much more expensive and
time-consuming than other applications and often involves manual labor from
multiple domain experts. In this work, we propose a multi-stage, self-paced
learning framework utilizing a convolutional neural network (CNN) to classify
Computed Tomography (CT) image patches. The key contribution of this approach
is that we augment the size of training samples by refining the unlabeled
instances with a self-paced learning CNN. By implementing the framework on high
performance computing servers including the NVIDIA DGX1 machine, we obtained
the experimental result, showing that the self-pace boosted network
consistently outperformed the original network even with very scarce manual
labels. The performance gain indicates that applications with limited training
samples such as medical image analysis can benefit from using the proposed
framework.Comment: accepted by 8th International Workshop on Machine Learning in Medical
Imaging (MLMI 2017
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