Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation

Machine learning-based imaging diagnostics has recently reached or even superseded the level of clinical experts in several clinical domains. However, classification decisions of a trained machine learning system are typically non-transparent, a major hindrance for clinical integration, error tracking or knowledge discovery. In this study, we present a transparent deep learning framework relying on convolutional neural networks (CNNs) and layer-wise relevance propagation (LRP) for diagnosing multiple sclerosis (MS). MS is commonly diagnosed utilizing a combination of clinical presentation and conventional magnetic resonance imaging (MRI), specifically the occurrence and presentation of white matter lesions in T2-weighted images. We hypothesized that using LRP in a naive predictive model would enable us to uncover relevant image features that a trained CNN uses for decision-making. Since imaging markers in MS are well-established this would enable us to validate the respective CNN model. First, we pre-trained a CNN on MRI data from the Alzheimer's Disease Neuroimaging Initiative (n = 921), afterwards specializing the CNN to discriminate between MS patients and healthy controls (n = 147). Using LRP, we then produced a heatmap for each subject in the holdout set depicting the voxel-wise relevance for a particular classification decision. The resulting CNN model resulted in a balanced accuracy of 87.04% and an area under the curve of 96.08% in a receiver operating characteristic curve. The subsequent LRP visualization revealed that the CNN model focuses indeed on individual lesions, but also incorporates additional information such as lesion location, non-lesional white matter or gray matter areas such as the thalamus, which are established conventional and advanced MRI markers in MS. We conclude that LRP and the proposed framework have the capability to make diagnostic decisions of..

Self-pruning Graph Neural Network for Predicting Inflammatory Disease Activity in Multiple Sclerosis from Brain MR Images

Multiple Sclerosis (MS) is a severe neurological disease characterized by inflammatory lesions in the central nervous system. Hence, predicting inflammatory disease activity is crucial for disease assessment and treatment. However, MS lesions can occur throughout the brain and vary in shape, size and total count among patients. The high variance in lesion load and locations makes it challenging for machine learning methods to learn a globally effective representation of whole-brain MRI scans to assess and predict disease. Technically it is non-trivial to incorporate essential biomarkers such as lesion load or spatial proximity. Our work represents the first attempt to utilize graph neural networks (GNN) to aggregate these biomarkers for a novel global representation. We propose a two-stage MS inflammatory disease activity prediction approach. First, a 3D segmentation network detects lesions, and a self-supervised algorithm extracts their image features. Second, the detected lesions are used to build a patient graph. The lesions act as nodes in the graph and are initialized with image features extracted in the first stage. Finally, the lesions are connected based on their spatial proximity and the inflammatory disease activity prediction is formulated as a graph classification task. Furthermore, we propose a self-pruning strategy to auto-select the most critical lesions for prediction. Our proposed method outperforms the existing baseline by a large margin (AUCs of 0.67 vs. 0.61 and 0.66 vs. 0.60 for one-year and two-year inflammatory disease activity, respectively). Finally, our proposed method enjoys inherent explainability by assigning an importance score to each lesion for the overall prediction. Code is available at https://github.com/chinmay5/ms_ida.gi

Similar neural pathways link psychological stress and brain-age in health and multiple sclerosis

Clinical and neuroscientific studies suggest a link between psychological stress and reduced brain health in health and neurological disease but it is unclear whether mediating pathways are similar. Consequently, we applied an arterial-spin-labeling MRI stress task in 42 healthy persons and 56 with multiple sclerosis, and investigated regional neural stress responses, associations between functional connectivity of stress-responsive regions and the brain-age prediction error, a highly sensitive machine learning brain health biomarker, and regional brain-age constituents in both groups. Stress responsivity did not differ between groups. Although elevated brain-age prediction errors indicated worse brain health in patients, anterior insula–occipital cortex (healthy persons: occipital pole; patients: fusiform gyrus) functional connectivity correlated with brain-age prediction errors in both groups. Finally, also gray matter contributed similarly to regional brain-age across groups. These findings might suggest a common stress–brain health pathway whose impact is amplified in multiple sclerosis by disease-specific vulnerability factors

Personalized Prediction of Future Lesion Activity and Treatment Effect in Multiple Sclerosis from Baseline MRI

Precision medicine for chronic diseases such as multiple sclerosis (MS) involves choosing a treatment which best balances efficacy and side effects/preferences for individual patients. Making this choice as early as possible is important, as delays in finding an effective therapy can lead to irreversible disability accrual. To this end, we present the first deep neural network model for individualized treatment decisions from baseline magnetic resonance imaging (MRI) (with clinical information if available) for MS patients. Our model (a) predicts future new and enlarging T2 weighted (NE-T2) lesion counts on follow-up MRI on multiple treatments and (b) estimates the conditional average treatment effect (CATE), as defined by the predicted future suppression of NE-T2 lesions, between different treatment options relative to placebo. Our model is validated on a proprietary federated dataset of 1817 multi-sequence MRIs acquired from MS patients during four multi-centre randomized clinical trials. Our framework achieves high average precision in the binarized regression of future NE-T2 lesions on five different treatments, identifies heterogeneous treatment effects, and provides a personalized treatment recommendation that accounts for treatment-associated risk (e.g. side effects, patient preference, administration difficulties).Comment: Accepted to MIDL 202

Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation

Machine learning-based imaging diagnostics has recently reached or even surpassed the level of clinical experts in several clinical domains. However, classification decisions of a trained machine learning system are typically non-transparent, a major hindrance for clinical integration, error tracking or knowledge discovery. In this study, we present a transparent deep learning framework relying on 3D convolutional neural networks (CNNs) and layer-wise relevance propagation (LRP) for diagnosing multiple sclerosis (MS), the most widespread autoimmune neuroinflammatory disease. MS is commonly diagnosed utilizing a combination of clinical presentation and conventional magnetic resonance imaging (MRI), specifically the occurrence and presentation of white matter lesions in T2-weighted images. We hypothesized that using LRP in a naive predictive model would enable us to uncover relevant image features that a trained CNN uses for decision-making. Since imaging markers in MS are well-established this would enable us to validate the respective CNN model. First, we pre-trained a CNN on MRI data from the Alzheimer's Disease Neuroimaging Initiative (n = 921), afterwards specializing the CNN to discriminate between MS patients (n = 76) and healthy controls (n = 71). Using LRP, we then produced a heatmap for each subject in the holdout set depicting the voxel-wise relevance for a particular classification decision. The resulting CNN model resulted in a balanced accuracy of 87.04% and an area under the curve of 96.08% in a receiver operating characteristic curve. The subsequent LRP visualization revealed that the CNN model focuses indeed on individual lesions, but also incorporates additional information such as lesion location, non-lesional white matter or gray matter areas such as the thalamus, which are established conventional and advanced MRI markers in MS. We conclude that LRP and the proposed framework have the capability to make diagnostic decisions of CNN models transparent, which could serve to justify classification decisions for clinical review, verify diagnosis-relevant features and potentially gather new disease knowledge

MSSEG-2 challenge proceedings: Multiple sclerosis new lesions segmentation challenge using a data management and processing infrastructure

International audienceThis proceedings book gathers methodological papers describing the segmenta-tion methods evaluated at the second MICCAI Challenge on Multiple Sclerosisnew lesions segmentation challenge using a data management and processinginfrastructure. This challenge took place as part of an effort of the OFSEP1(French registry on multiple sclerosis aiming at gathering, for research purposes,imaging data, clinical data and biological samples from the French populationof multiple sclerosis subjects) and FLI2(France Life Imaging, devoted to setupa national distributed e-infrastructure to manage and process medical imagingdata). These joint efforts are directed towards automatic segmentation of MRIscans of MS patients to help clinicians in their daily practice. This challengetook place at the MICCAI 2021 conference, on September 23rd 2021.More precisely, the problem addressed in this challenge is as follows. Con-ventional MRI is widely used for disease diagnosis, patient follow-up, monitoringof therapies, and more generally for the understanding of the natural history ofMS. A growing literature is interested in the delineation of new MS lesions onT2/FLAIR by comparing one time point to another. This marker is even morecrucial than the total number and volume of lesions as the accumulation of newlesions allows clinicians to know if a given anti-inflammatory DMD (disease mod-ifying drug) works for the patient. The only indicator of drug efficacy is indeedthe absence of new T2 lesions within the central nervous system. Performingthis new lesions count by hand is however a very complex and time consumingtask. Automating the detection of these new lesions would therefore be a majoradvance for evaluating the patient disease activity.Based on the success of the first MSSEG challenge, we have organized aMICCAI sponsored online challenge, this time on new MS lesions detection3.This challenge has allowed to 1) estimate the progress performed during the2016 - 2021 period, 2) extend the number of patients, and 3) focus on the newlesions crucial clinical marker. We have performed the evaluation task on a largedatabase (100 patients, each with two time points) compiled from the OFSEPcohort with 3D FLAIR images from different centers and scanners. As in ourprevious challenge, we have conducted the evaluation on a dedicated platform(FLI-IAM) to automate the evaluation and remove the potential biases due tochallengers seeing the images on which the evaluation is made

Explainable deep learning classifiers for disease detection based on structural brain MRI data

In dieser Doktorarbeit wird die Frage untersucht, wie erfolgreich deep learning bei der Diagnostik von neurodegenerativen Erkrankungen unterstützen kann. In 5 experimentellen Studien wird die Anwendung von Convolutional Neural Networks (CNNs) auf Daten der Magnetresonanztomographie (MRT) untersucht. Ein Schwerpunkt wird dabei auf die Erklärbarkeit der eigentlich intransparenten Modelle gelegt. Mit Hilfe von Methoden der erklärbaren künstlichen Intelligenz (KI) werden Heatmaps erstellt, die die Relevanz einzelner Bildbereiche für das Modell darstellen. Die 5 Studien dieser Dissertation zeigen das Potenzial von CNNs zur Krankheitserkennung auf neurologischen MRT, insbesondere bei der Kombination mit Methoden der erklärbaren KI. Mehrere Herausforderungen wurden in den Studien aufgezeigt und Lösungsansätze in den Experimenten evaluiert. Über alle Studien hinweg haben CNNs gute Klassifikationsgenauigkeiten erzielt und konnten durch den Vergleich von Heatmaps zur klinischen Literatur validiert werden. Weiterhin wurde eine neue CNN Architektur entwickelt, spezialisiert auf die räumlichen Eigenschaften von Gehirn MRT Bildern.Deep learning and especially convolutional neural networks (CNNs) have a high potential of being implemented into clinical decision support software for tasks such as diagnosis and prediction of disease courses. This thesis has studied the application of CNNs on structural MRI data for diagnosing neurological diseases. Specifically, multiple sclerosis and Alzheimer’s disease were used as classification targets due to their high prevalence, data availability and apparent biomarkers in structural MRI data. The classification task is challenging since pathology can be highly individual and difficult for human experts to detect and due to small sample sizes, which are caused by the high acquisition cost and sensitivity of medical imaging data. A roadblock in adopting CNNs to clinical practice is their lack of interpretability. Therefore, after optimizing the machine learning models for predictive performance (e.g. balanced accuracy), we have employed explainability methods to study the reliability and validity of the trained models. The deep learning models achieved good predictive performance of over 87% balanced accuracy on all tasks and the explainability heatmaps showed coherence with known clinical biomarkers for both disorders. Explainability methods were compared quantitatively using brain atlases and shortcomings regarding their robustness were revealed. Further investigations showed clear benefits of transfer-learning and image registration on the model performance. Lastly, a new CNN layer type was introduced, which incorporates a prior on the spatial homogeneity of neuro-MRI data. CNNs excel when used on natural images which possess spatial heterogeneity, and even though MRI data and natural images share computational similarities, the composition and orientation of neuro-MRI is very distinct. The introduced patch-individual filter (PIF) layer breaks the assumption of spatial invariance of CNNs and reduces convergence time on different data sets without reducing predictive performance. The presented work highlights many challenges that CNNs for disease diagnosis face on MRI data and defines as well as tests strategies to overcome those

Precision Monitoring for Disease Progression in Patients with Multiple Sclerosis: A Deep Learning Approach

Artificial intelligence has tremendous potential in a range of clinical applications. Leveraging recent advances in deep learning, the works in this thesis has generated a range of technologies for patients with Multiple Sclerosis (MS) that facilitate precision monitoring using routine MRI and clinical assessments; and contribute to realising the goal of personalised disease management. MS is a chronic inflammatory demyelinating disease of the central nervous system (CNS), characterised by focal demyelinating plaques in the brain and spinal cord; and progressive neurodegeneration. Despite success in cohort studies and clinical trials, the measurement of disease activity using conventional imaging biomarkers in real-world clinical practice is limited to qualitative assessment of lesion activity, which is time consuming and prone to human error. Quantitative measures, such as T2 lesion load, volumetric assessment of lesion activity and brain atrophy, are constrained by challenges associated with handling real-world data variances. In this thesis, DeepBVC was developed for robust brain atrophy assessment through imaging synthesis, while a lesion segmentation model was developed using a novel federated learning framework, Fed-CoT, to leverage large data collaborations. With existing quantitative brain structural analyses, this work has developed an effective deep learning analysis pipeline, which delivers a fully automated suite of MS-specific clinical imaging biomarkers to facilitate the precision monitoring of patients with MS and response to disease modifying therapy. The framework for individualised MRI-guided management in this thesis was complemented by a disease prognosis model, based on a Large Language Model, providing insights into the risks of clinical worsening over the subsequent 3 years. The value and performance of the MS biomarkers in this thesis are underpinned by extensive validation in real-world, multi-centre data from more than 1030 patients

Applications of Deep Learning Techniques for Automated Multiple Sclerosis Detection Using Magnetic Resonance Imaging: A Review

Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor problems for people with a detrimental effect on the functioning of the nervous system. In order to diagnose MS, multiple screening methods have been proposed so far; among them, magnetic resonance imaging (MRI) has received considerable attention among physicians. MRI modalities provide physicians with fundamental information about the structure and function of the brain, which is crucial for the rapid diagnosis of MS lesions. Diagnosing MS using MRI is time-consuming, tedious, and prone to manual errors. Research on the implementation of computer aided diagnosis system (CADS) based on artificial intelligence (AI) to diagnose MS involves conventional machine learning and deep learning (DL) methods. In conventional machine learning, feature extraction, feature selection, and classification steps are carried out by using trial and error; on the contrary, these steps in DL are based on deep layers whose values are automatically learn. In this paper, a complete review of automated MS diagnosis methods performed using DL techniques with MRI neuroimaging modalities is provided. Initially, the steps involved in various CADS proposed using MRI modalities and DL techniques for MS diagnosis are investigated. The important preprocessing techniques employed in various works are analyzed. Most of the published papers on MS diagnosis using MRI modalities and DL are presented. The most significant challenges facing and future direction of automated diagnosis of MS using MRI modalities and DL techniques are also provided