In this paper, we present ClinicaDL, an open-source software platform that aims at enhancing the reproducibility and rigor of research for deep learning in neuroimaging. We first provide an overview of the software platform and then focus on recent advances. Features of the software aim at addressing three key issues in the field: the lack of reproducibility, the methodological flaws that plague many published studies and the difficulties using neuroimaging datasets for people with little expertise in this application area. Key existing functionalities include automatic data splitting, checking for data leakage, standards for data organization and results storing, continuous integration and integration with Clinica for preprocessing, amongst others. The most prominent recent features are as follows. We now provide various data augmentation and synthetic data generation functions (both standard and advanced ones including motion and hypometabolism simulation). Continuous integration test data are now versioned using DVC (data version control). Tools for generating validation splits have been made more generic. We made major improvements regarding usability and performance. We now support multi-GPU training and automatic mixed precision (to exploit tensor cores). We created a graphical interface to easily generate training specifications. We allow tracking of experiments through standard tools (MLflow, Weights&Biases). We believe that ClinicaDL can contribute to enhance the trustworthiness of research in deep learning for neuroimaging. Moreover, its functionalities and coding practices may serve as inspiration for the whole medical imaging community, beyond neuroimaging.
As deep learning has been widely used for computer aided-diagnosis, we wished to know whether attribution maps obtained using gradient back-propagation could correctly highlight the patterns of disease subtypes discovered by a deep learning classifier. As the correctness of attribution maps is difficult to evaluate directly on medical images, we used synthetic data mimicking the difference between brain MRI of controls and demented patients to design more reliable evaluation criteria of attribution maps. We demonstrated that attribution maps may mix the regions associated with different subtypes for small data sets while they could accurately characterize both subtypes using a large data set. We then proposed simple data augmentation techniques and showed that they could improve the coherence of the explanations for a small data set. .
Deep learning methods have shown a high performance potential for medical image analysis [1], particularly classification for computer-aided diagnosis. However, explaining their decisions is not trivial and could be helpful to achieve better results and know how far they can be trusted. Many methods have been developed in order to explain the decisions of classifiers [2]–[7], but their outputs are not always meaningful and remain difficult to interpret. In this paper, we adapted the method of [8] to 3D medical images to find on which basis a network classifies quantitative data. Indeed, quantitative data can be obtained from different medical imaging modalities, for example binding potential maps obtained with positron emission tomography (PET) or gray matter (GM) probability maps extracted from structural magnetic resonance imaging (MRI). Our application focuses on the detection of Alzheimer’s disease (AD), a neurodegenerative syndrome that induces GM atrophy. We used as inputs GM probability maps, a proxy for atrophy, extracted from T1-weighted (T1w) MRI. The process includes two distinct parts: first a convolutional neural network (CNN) is trained to classify AD from control subjects, then the weights of the network are fixed and a mask is trained to prevent the network from classifying correctly all the subjects it has correctly classified after training. The goals of this work are to assess whether the visualization method initially developed for natural images is suitable for 3D medical images and to exploit it to better understand the decisions taken by classification networks. This work is an original work and has not been submitted elsewhere.
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