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Found 17 papers, 4 papers with code
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).
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.
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.
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.
To Learn or Not to Learn Features for Deformable Registration?
Feature-based registration has been popular with a variety of features ranging from voxel intensity to Self-Similarity Context (SSC).
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.
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.
Sub-cortical structure segmentation database for young population
Segmentation of sub-cortical structures from MRI scans is of interest in many neurological diagnosis.
NormCo: Deep Disease Normalization for Biomedical Knowledge Base Construction
Biomedical knowledge bases are crucial in modern data-driven biomedical sciences, but auto-mated biomedical knowledge base construction remains challenging.
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).
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.
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.
You Only Need a Good Embeddings Extractor to Fix Spurious Correlations
With experiments on a wide range of pre-trained models and pre-training datasets, we show that the capacity of the pre-training model and the size of the pre-training dataset matters.
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.
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.
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.
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.
