352 research outputs found
On explaining machine learning models by evolving crucial and compact features
Feature construction can substantially improve the accuracy of Machine Learning (ML) algorithms. Genetic Programming (GP) has been proven to be effective at this task by evolving non-linear combinations of input features. GP additionally has the potential to improve ML explainability since explicit expressions are evolved. Yet, in most GP works the complexity of evolved features is not explicitly bound or minimized though this is arguably key for explainability. In this article, we assess to what extent GP still performs favorably at feature construction when constructing features that are (1) Of small-enough number, to enable visualization of the behavior of the ML model; (2) Of small-enough size, to enable interpretability of the features themselves; (3) Of sufficient informative power, to retain or even improve the performance of the ML algorithm. We consider a simple feature construction scheme using three different GP algorithms, as well as random search, to evolve features for five ML algorithms, including support vector machines and random forest. Our results on 21 datasets pertaining to classification and regression problems show that constructing only two compact features can be sufficient to rival the use of the entire original feature set. We further find that a modern GP algorithm, GP-GOMEA, performs best overall. These results, combined with examples that we provide of readable constructed features and of 2D visualizations of ML behavior, lead us to positively conclude that GP-based feature construction still works well when explicitly searching for compact features, making it extremely helpful to explain ML models
Observer variation-aware medical image segmentation by combining deep learning and surrogate-assisted genetic algorithms
There has recently been great progress in automatic segmentation of medical
images with deep learning algorithms. In most works observer variation is
acknowledged to be a problem as it makes training data heterogeneous but so far
no attempts have been made to explicitly capture this variation. Here, we
propose an approach capable of mimicking different styles of segmentation,
which potentially can improve quality and clinical acceptance of automatic
segmentation methods. In this work, instead of training one neural network on
all available data, we train several neural networks on subgroups of data
belonging to different segmentation variations separately. Because a priori it
may be unclear what styles of segmentation exist in the data and because
different styles do not necessarily map one-on-one to different observers, the
subgroups should be automatically determined. We achieve this by searching for
the best data partition with a genetic algorithm. Therefore, each network can
learn a specific style of segmentation from grouped training data. We provide
proof of principle results for open-sourced prostate segmentation MRI data with
simulated observer variations. Our approach provides an improvement of up to
23% (depending on simulated variations) in terms of Dice and surface Dice
coefficients compared to one network trained on all data.Comment: 11 pages, 5 figures, SPIE Medical Imaging Conference - 202
An End-to-end Deep Learning Approach for Landmark Detection and Matching in Medical Images
Anatomical landmark correspondences in medical images can provide additional
guidance information for the alignment of two images, which, in turn, is
crucial for many medical applications. However, manual landmark annotation is
labor-intensive. Therefore, we propose an end-to-end deep learning approach to
automatically detect landmark correspondences in pairs of two-dimensional (2D)
images. Our approach consists of a Siamese neural network, which is trained to
identify salient locations in images as landmarks and predict matching
probabilities for landmark pairs from two different images. We trained our
approach on 2D transverse slices from 168 lower abdominal Computed Tomography
(CT) scans. We tested the approach on 22,206 pairs of 2D slices with varying
levels of intensity, affine, and elastic transformations. The proposed approach
finds an average of 639, 466, and 370 landmark matches per image pair for
intensity, affine, and elastic transformations, respectively, with spatial
matching errors of at most 1 mm. Further, more than 99% of the landmark pairs
are within a spatial matching error of 2 mm, 4 mm, and 8 mm for image pairs
with intensity, affine, and elastic transformations, respectively. To
investigate the utility of our developed approach in a clinical setting, we
also tested our approach on pairs of transverse slices selected from follow-up
CT scans of three patients. Visual inspection of the results revealed landmark
matches in both bony anatomical regions as well as in soft tissues lacking
prominent intensity gradients.Comment: SPIE Medical Imaging Conference - 202
Bi-objective optimization of organ properties for the simulation of intracavitary brachytherapy applicator placement in cervical cancer
Validation of deformable image registration techniques is extremely
important, but hard, especially when complex deformations or content mismatch
are involved. These complex deformations and content mismatch, for example,
occur after the placement of an applicator for brachytherapy for cervical
cancer. Virtual phantoms could enable the creation of validation data sets with
ground truth deformations that simulate the large deformations that occur
between image acquisitions. However, the quality of the multi-organ Finite
Element Method (FEM)-based simulations is dependent on the patient-specific
external forces and mechanical properties assigned to the organs. A common
approach to calibrate these simulation parameters is through optimization,
finding the parameter settings that optimize the match between the outcome of
the simulation and reality. When considering inherently simplified organ
models, we hypothesize that the optimal deformations of one organ cannot be
achieved with a single parameter setting without compromising the optimality of
the deformation of the surrounding organs. This means that there will be a
trade-off between the optimal deformations of adjacent organs, such as the
vagina-uterus and bladder. This work therefore proposes and evaluates a
multi-objective optimization approach where the trade-off between organ
deformations can be assessed after optimization. We showcase what the extent of
the trade-off looks like when bi-objectively optimizing the patient-specific
mechanical properties and external forces of the vagina-uterus and bladder for
FEM-based simulations
Predictors for outcome of failure of balloon dilatation in patients with achalasia
Background: Pneumatic balloon dilatation (PD) is a regular treatment modality for achalasia. The reported success rates of PD vary. Recurrent symptoms often require repeated PD or surgery. Objective: To identify predicting factors for symptom recurrence requiring repeated treatment. Methods: Between 1974 and 2006, 336 patients were treated with PD and included in this longitudinal cohort study. The median follow-up was 129 months (range 1-378). Recurrence of achalasia was defined as symptom recurrence in combination with increased lower oesophageal sphincter (LOS) pressure on manometry, requiring repeated treatment. Patient characteristics, results of timed barium oesophagram and manometry as well as baseline PD characteristics were evaluated as predictors of disease recurrence with Kaplan-Meier curves and Cox regression analysis. Results: 111 patients had symptom recurrence requiring repeated treatment. Symptoms recurred after a mean follow-up of 51 months (range 1-348). High recurrence percentages were found in patients younger than 21 years in whom the 5 and 10-year risks of recurrence were 64% and 72%, respectively. These risks were respectively 28% and 36% in patients with classic achalasia, respectively 48% and 60% in patients without complete obliteration of the balloon's waist during PD and respectively 25% and 33% in patients with a LOS pressure greater than 10 mm Hg at 3 months post-dilatation. These four predictors remained statistically significant in a multivariable Cox analysis. Conclusion: Although PD is an effective primary treatment in patients with primary achalasia, patients are at risk of recurrent disease, with this risk increasing during long-term follow-up. Young age at presentation, classic achalasia, high LOS pressure 3 months after PD and incomplete obliteration of the balloon's waist during PD are the most important predicting factors for the need for repeated treatment during follow-up. Patients who meet one or more of these characteristics may be considered earlier for alternative treatment, such as surgery
A first step toward uncovering the truth about weight tuning in deformable image registration
Deformable image registration is currently predominantly solved by optimizing a weighted linear combination of objectives. Successfully tuning the weights associated with these objectives is not trivial, leading to trial-and-error approaches. Such an approach assumes an intuitive interplay between weights, optimization objectives, and target registration errors. However, it is not known whether this always holds for existing registration methods. To investigate the interplay between weights, optimization objectives, and registration errors, we employ multi-objective optimization. Here, objectives of interest are optimized simultaneously, causing a set of multiple optimal solutions to exist, called the optimal Pareto front. Our medical application is in breast cancer and includes the challenging prone-supine registration problem. In total, we studied the interplay in three different ways. First, we ran many random linear combinations of objectives using the well-known registration software elastix. Second, since the optimization algorithms used in registration are typically of a local-search nature, final solutions may not always form a Pareto front. We therefore employed a multi-objective evolutionary algorithm that finds weights that correspond to registration outcomes that do form a Pareto front. Third, we examined how the interplay differs if a true multi-objective (i.e., weight-free) image registration method is used. Results indicate that a trial-and-error weight-adaptation approach can be successful for the easy prone to prone breast image registration case, due to the absence of many local optima. With increasing problem difficulty the use of more advanced approaches can be of value in finding and selecting the optimal registration outcomes
Diversifying Multi-Objective Gradient Techniques and their Role in Hybrid Multi-Objective Evolutionary Algorithms for Deformable Medical Image Registration
Gradient methods and their value in single-objective, real-valued
optimization are well-established. As such, they play
a key role in tackling real-world, hard optimization problems
such as deformable image registration (DIR). A key question
is to which extent gradient techniques can also play a role in
a multi-objective approach to DIR. We therefore aim to exploit
gradient information within an evolutionary-algorithm-based
multi-objective optimization framework for DIR. Although an
analytical description of the multi-objective gradient (the set
of all Pareto-optimal improving directions) is
available, it is nontrivial how to best choose the most
appropriate direction per solution because these directions are
not necessarily uniformly distributed in objective space. To
address this, we employ a Monte-Carlo method to obtain
a discrete, spatially-uniformly distributed approximation of
the set of Pareto-optimal improving directions. We then
apply a diversification technique in which each solution is
associated with a unique direction from this set based on its
multi- as well as single-objective rank. To assess its utility,
we compare a state-of-the-art multi-objective evolutionary
algorithm with three different hybrid versions thereof on
several benchmark problems and two medical DIR problems.
Results show that the diversification strategy successfully
leads to unbiased improvement, helping an adaptive hybrid
scheme solve all problems, but the evolutionary algorithm
remains the most powerful optimization method, providing
the best balance between proximity and diversity
On the usefulness of gradient information in multi-objective deformable image registration using a B-spline-based dual-dynamic transformation model: comparison of three optimization algorithms
The use of gradient information is well-known to be highly useful in single-objective optimization-based image
registration methods. However, its usefulness has not yet been investigated for deformable image registration from a
multi-objective optimization perspective. To this end, within a previously introduced multi-objective optimization
framework, we use a smooth B-spline-based dual-dynamic transformation model that allows us to derive gradient
information analytically, while still being able to account for large deformations. Within the multi-objective framework,
we previously employed a powerful evolutionary algorithm (EA) that computes and advances multiple outcomes at once,
resulting in a set of solutions (a so-called Pareto front) that represents efficient trade-offs between the objectives. With
the addition of the B-spline-based transformation model, we studied the usefulness of gradient information in multiobjective
deformable image registration using three different optimization algorithms: the (gradient-less) EA, a gradientonly
algorithm, and a hybridization of these two. We evaluated the algorithms to register highly deformed images: 2D
MRI slices of the breast in prone and supine positions. Results demonstrate that gradient-based multi-objective
optimization significantly speeds up optimization in the initial stages of optimization. However, allowing sufficient
computational resources, better results could still be obtained with the EA. Ultimately, the hybrid EA found the best
overall approximation of the optimal Pareto front, further indicating that adding gradient-based optimization for multiobjective optimization-based deformable image registration can indeed be beneficial
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