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Found 38 papers, 13 papers with code
Probabilistic Temporal Prediction of Continuous Disease Trajectories and Treatment Effects Using Neural SDEs
Personalized medicine based on medical images, including predicting future individualized clinical disease progression and treatment response, would have an enormous impact on healthcare and drug development, particularly for diseases (e. g. multiple sclerosis (MS)) with long term, complex, heterogeneous evolutions and no cure.
DeCoDEx: Confounder Detector Guidance for Improved Diffusion-based Counterfactual Explanations
Deep learning classifiers are prone to latching onto dominant confounders present in a dataset rather than on the causal markers associated with the target class, leading to poor generalization and biased predictions.
HyperFusion: A Hypernetwork Approach to Multimodal Integration of Tabular and Medical Imaging Data for Predictive Modeling
The integration of diverse clinical modalities such as medical imaging and the tabular data obtained by the patients' Electronic Health Records (EHRs) is a crucial aspect of modern healthcare.
Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensembles
However, perturbations in the input images at test time (e. g. noise, domain shifts) can still lead to significant performance degradation, posing challenges for trustworthy clinical deployment.
Debiasing Counterfactuals In the Presence of Spurious Correlations
Deep learning models can perform well in complex medical imaging classification tasks, even when basing their conclusions on spurious correlations (i. e. confounders), should they be prevalent in the training dataset, rather than on the causal image markers of interest.
Mitigating Calibration Bias Without Fixed Attribute Grouping for Improved Fairness in Medical Imaging Analysis
However, models that are well calibrated overall can still be poorly calibrated for a sub-population, potentially resulting in a clinician unwittingly making poor decisions for this group based on the recommendations of the model.
Improving Image-Based Precision Medicine with Uncertainty-Aware Causal Models
We evaluate the correlation of the uncertainty estimate with the factual error, and, given the lack of ground truth counterfactual outcomes, demonstrate how uncertainty for the ITE prediction relates to bounds on the ITE error.
Evaluating the Fairness of Deep Learning Uncertainty Estimates in Medical Image Analysis
Unfortunately, recent studies have indeed shown significant biases in DL models across demographic subgroups (e. g., race, sex, age) in the context of medical image analysis, indicating a lack of fairness in the models.
Understanding metric-related pitfalls in image analysis validation
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice.
Clinically Plausible Pathology-Anatomy Disentanglement in Patient Brain MRI with Structured Variational Priors
We propose a hierarchically structured variational inference model for accurately disentangling observable evidence of disease (e. g. brain lesions or atrophy) from subject-specific anatomy in brain MRIs.
Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentation
This is particularly important in the context of medical image segmentation of pathological structures (e. g. lesions), where the annotation process is much more subjective, and affected by a number underlying factors, including the annotation protocol, rater education/experience, and clinical aims, among others.
On Learning Fairness and Accuracy on Multiple Subgroups
In the upper-level, the fair predictor is updated to be close to all subgroup specific predictors.
Heatmap Regression for Lesion Detection using Pointwise Annotations
In many clinical contexts, detecting all lesions is imperative for evaluating disease activity.
Counterfactual Image Synthesis for Discovery of Personalized Predictive Image Markers
In this work, we demonstrate that data-driven biomarker discovery can be achieved through a counterfactual synthesis process.
Information Gain Sampling for Active Learning in Medical Image Classification
This work presents an information-theoretic active learning framework that guides the optimal selection of images from the unlabelled pool to be labeled based on maximizing the expected information gain (EIG) on an evaluation dataset.
Metrics reloaded: Recommendations for image analysis validation
The framework was developed in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint - a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output.
Segmentation-Consistent Probabilistic Lesion Counting
Lesion counts are important indicators of disease severity, patient prognosis, and treatment efficacy, yet counting as a task in medical imaging is often overlooked in favor of segmentation.
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.
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation.
Cohort Bias Adaptation in Aggregated Datasets for Lesion Segmentation
Many automatic machine learning models developed for focal pathology (e. g. lesions, tumours) detection and segmentation perform well, but do not generalize as well to new patient cohorts, impeding their widespread adoption into real clinical contexts.
Optimizing Operating Points for High Performance Lesion Detection and Segmentation Using Lesion Size Reweighting
There are many clinical contexts which require accurate detection and segmentation of all focal pathologies (e. g. lesions, tumours) in patient images.
Common Limitations of Image Processing Metrics: A Picture Story
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation.
HAD-Net: A Hierarchical Adversarial Knowledge Distillation Network for Improved Enhanced Tumour Segmentation Without Post-Contrast Images
The proposed network is trained and tested on the BraTS 2019 brain tumour segmentation challenge dataset, where it achieves performance improvements in the ranges of 16% - 26% over (a) recent modality-agnostic segmentation methods (U-HeMIS, U-HVED), (b) KD-Net adapted to this problem, (c) the pre-trained student network and (d) a non-hierarchical version of the network (AD-Net), in terms of Dice scores for enhancing tumour (ET).
Accounting for Variance in Machine Learning Benchmarks
Strong empirical evidence that one machine-learning algorithm A outperforms another one B ideally calls for multiple trials optimizing the learning pipeline over sources of variation such as data sampling, data augmentation, parameter initialization, and hyperparameters choices.
Optimization over Random and Gradient Probabilistic Pixel Sampling for Fast, Robust Multi-Resolution Image Registration
This paper presents an approach to fast image registration through probabilistic pixel sampling.
Uncertainty driven probabilistic voxel selection for image registration
This paper presents a novel probabilistic voxel selection strategy for medical image registration in time-sensitive contexts, where the goal is aggressive voxel sampling (e. g. using less than 1% of the total number) while maintaining registration accuracy and low failure rate.
Grow-Push-Prune: aligning deep discriminants for effective structural network compression
We also show that our grown Inception nets (without hard-coded dimension alignment) clearly outperform residual nets of similar complexities.
Medical Imaging with Deep Learning: MIDL 2020 -- Short Paper Track
This compendium gathers all the accepted extended abstracts from the Third International Conference on Medical Imaging with Deep Learning (MIDL 2020), held in Montreal, Canada, 6-9 July 2020.
Uncertainty Evaluation Metric for Brain Tumour Segmentation
In this paper, we develop a metric designed to assess and rank uncertainty measures for the task of brain tumour sub-tissue segmentation in the BraTS 2019 sub-challenge on uncertainty quantification.
Exploring Bayesian Deep Learning Uncertainty Measures for Segmentation of New Lesions in Longitudinal MRIs
In this paper, we develop a modified U-Net architecture to accurately segment new and enlarging lesions in longitudinal MRI, based on multi-modal MRI inputs, as well as subtrac- tion images between timepoints, in the context of large-scale clinical trial data for patients with Multiple Sclerosis (MS).
BIAS: Transparent reporting of biomedical image analysis challenges
The number of biomedical image analysis challenges organized per year is steadily increasing.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation
We present the first exploration of multiple uncertainty estimates based on Monte Carlo (MC) dropout [4] in the context of deep networks for lesion detection and segmentation in medical images.
RS-Net: Regression-Segmentation 3D CNN for Synthesis of Full Resolution Missing Brain MRI in the Presence of Tumours
Accurate synthesis of a full 3D MR image containing tumours from available MRI (e. g. to replace an image that is currently unavailable or corrupted) would provide a clinician as well as downstream inference methods with important complementary information for disease analysis.
Why rankings of biomedical image analysis competitions should be interpreted with care
International challenges have become the standard for validation of biomedical image analysis methods.
Task dependent Deep LDA pruning of neural networks
Moreover, we examine our approach's potential in network architecture search for specific tasks and analyze the influence of our pruning on model robustness to noises and adversarial attacks.
Efficient Gender Classification Using a Deep LDA-Pruned Net
Many real-time tasks, such as human-computer interaction, require fast and efficient facial gender classification.
Iterative Multilevel MRF Leveraging Context and Voxel Information for Brain Tumour Segmentation in MRI
In this paper, we introduce a fully automated multistage graphical probabilistic framework to segment brain tumours from multimodal Magnetic Resonance Images (MRIs) acquired from real patients.
