35 research outputs found
Convergence Analysis of the Hessian Estimation Evolution Strategy
The class of algorithms called Hessian Estimation Evolution Strategies
(HE-ESs) update the covariance matrix of their sampling distribution by
directly estimating the curvature of the objective function. The approach is
practically efficient, as attested by respectable performance on the BBOB
testbed, even on rather irregular functions.
In this paper we formally prove two strong guarantees for the (1+4)-HE-ES, a
minimal elitist member of the family: stability of the covariance matrix
update, and as a consequence, linear convergence on all convex quadratic
problems at a rate that is independent of the problem instance
Convolutional neural networks for segmentation and object detection of human semen
We compare a set of convolutional neural network (CNN) architectures for the
task of segmenting and detecting human sperm cells in an image taken from a
semen sample. In contrast to previous work, samples are not stained or washed
to allow for full sperm quality analysis, making analysis harder due to
clutter. Our results indicate that training on full images is superior to
training on patches when class-skew is properly handled. Full image training
including up-sampling during training proves to be beneficial in deep CNNs for
pixel wise accuracy and detection performance. Predicted sperm cells are found
by using connected components on the CNN predictions. We investigate
optimization of a threshold parameter on the size of detected components. Our
best network achieves 93.87% precision and 91.89% recall on our test dataset
after thresholding outperforming a classical mage analysis approach.Comment: Submitted for Scandinavian Conference on Image Analysis 201
A Loss Function for Generative Neural Networks Based on Watson's Perceptual Model
To train Variational Autoencoders (VAEs) to generate realistic imagery
requires a loss function that reflects human perception of image similarity. We
propose such a loss function based on Watson's perceptual model, which computes
a weighted distance in frequency space and accounts for luminance and contrast
masking. We extend the model to color images, increase its robustness to
translation by using the Fourier Transform, remove artifacts due to splitting
the image into blocks, and make it differentiable. In experiments, VAEs trained
with the new loss function generated realistic, high-quality image samples.
Compared to using the Euclidean distance and the Structural Similarity Index,
the images were less blurry; compared to deep neural network based losses, the
new approach required less computational resources and generated images with
less artifacts.Comment: Published at the 34th Conference on Neural Information Processing
Systems (NeurIPS 2020
Multimodal Variational Autoencoders for Semi-Supervised Learning: In Defense of Product-of-Experts
Multimodal generative models should be able to learn a meaningful latent
representation that enables a coherent joint generation of all modalities
(e.g., images and text). Many applications also require the ability to
accurately sample modalities conditioned on observations of a subset of the
modalities. Often not all modalities may be observed for all training data
points, so semi-supervised learning should be possible. In this study, we
evaluate a family of product-of-experts (PoE) based variational autoencoders
that have these desired properties. We include a novel PoE based architecture
and training procedure. An empirical evaluation shows that the PoE based models
can outperform an additive mixture-of-experts (MoE) approach. Our experiments
support the intuition that PoE models are more suited for a conjunctive
combination of modalities while MoEs are more suited for a disjunctive fusion
Reducing Annotation Need in Self-Explanatory Models for Lung Nodule Diagnosis
Feature-based self-explanatory methods explain their classification in terms
of human-understandable features. In the medical imaging community, this
semantic matching of clinical knowledge adds significantly to the
trustworthiness of the AI. However, the cost of additional annotation of
features remains a pressing issue. We address this problem by proposing
cRedAnno, a data-/annotation-efficient self-explanatory approach for lung
nodule diagnosis. cRedAnno considerably reduces the annotation need by
introducing self-supervised contrastive learning to alleviate the burden of
learning most parameters from annotation, replacing end-to-end training with
two-stage training. When training with hundreds of nodule samples and only 1%
of their annotations, cRedAnno achieves competitive accuracy in predicting
malignancy, meanwhile significantly surpassing most previous works in
predicting nodule attributes. Visualisation of the learned space further
indicates that the correlation between the clustering of malignancy and nodule
attributes coincides with clinical knowledge. Our complete code is open-source
available: https://github.com/diku-dk/credanno.Comment: 10 pages, 4 figures, 2 table
Semi-supervised, Topology-Aware Segmentation of Tubular Structures from Live Imaging 3D Microscopy
Impact of device programming on the success of the first anti-tachycardia pacing therapy:An anonymized large-scale study
BackgroundAntitachycardia pacing (ATP) is an effective treatment for ventricular tachycardia (VT). We evaluated the efficacy of different ATP programs based on a large remote monitoring data set from patients with implantable cardioverter-defibrillators (ICDs).MethodsA dataset from 18,679 ICD patients was used to evaluate the first delivered ATP treatment. We considered all device programs that were used for at least 50 patients, leaving us with 7 different programs and a total of 32,045 episodes. We used the two-proportions z-test (α = 0.01) to compare the probability of success and the probability for acceleration in each group with the corresponding values of the default setting.ResultsOverall, the first ATP treatment terminated in 78.4%-97.5% of episodes with slow VT and 81.5%-91.1% of episodes with fast VT. The default setting of the ATP programs with the number of sequences S = 3 was applied to treat 30.1% of the slow and 36.6% of the fast episodes. Reducing the maximum number of sequences to S = 2 decreased the success rate for slow VT (P ConclusionWhile the default programs performed well, we found that increasing the number of sequences from 3 to 4 was a promising option to improve the overall ATP performance
Estimation of Convex Polytopes for Automatic Discovery of Charge State Transitions in Quantum Dot Arrays
In spin based quantum dot arrays, material or fabrication imprecisions affect the behaviour of the device, which must be taken into account when controlling it. This requires measuring the shape of specific convex polytopes. We present an algorithm that automatically discovers count, shape and size of the facets of a convex polytope from measurements by alternating a phase of model-fitting with a phase of querying new measurements, based on the fitted model. We evaluate the algorithm on simulated polytopes and devices, as well as a real 2 × 2 spin qubit array. Results show that we can reliably find the facets of the convex polytopes, including small facets with sizes on the order of the measurement precision