43 research outputs found
Enhancing cardiac image segmentation through persistent homology regularization
Treballs Finals de Grau d'Enginyeria Informà tica, Facultat de Matemà tiques, Universitat de Barcelona, Any: 2022, Director: Sergio Escalera Guerrero, Carles Casacuberta i Rubén Ballester Bautista[en] Cardiovascular diseases are a major cause of death and disability. Deep learning-based segmentation methods could help to reduce their severity by aiding in early diagnosing but high levels of accuracy are necessary. The vast majority of methods focus on correcting local errors and miss the global picture. To ad-
dress this issue, researchers have developed techniques that incorporate global context and consider the relationships between pixels. Here, we apply persistent homology, a branch of topology that studies the topological structure of shapes, along with deep learning methods to improve the heart segmentation. We use multidimensional topological losses to avoid spurious components and holes and increase the total accuracy. We evaluate the performance of three different approaches: using the dice and pixel-wise losses with the sum of persistences of label diagrams as a regularizer, using the dice and pixel-wise losses with the bottleneck
distance as a regularizer, and using both losses without any regularization. We find that, while more computationally demanding, the methods using topological regularizers outperform the other method in terms of accuracy
Using topological data analysis for building Bayesan neural networks
For the first time, a simplified approach to constructing Bayesian neural networks is proposed, combining computational
efficiency with the ability to analyze the learning process. The proposed approach is based on Bayesianization of a
deterministic neural network by randomizing parameters only at the interface level, i.e., the formation of a Bayesian
neural network based on a given network by replacing its parameters with probability distributions that have the
parameters of the original model as the average value. Evaluations of the efficiency metrics of the neural network were
obtained within the framework of the approach under consideration, and the Bayesian neural network constructed
through variation inference were performed using topological data analysis methods. The Bayesianization procedure
is implemented through graded variation of the randomization intensity. As an alternative, two neural networks with
identical structure were used — deterministic and classical Bayesian networks. The input of the neural network was
supplied with the original data of two datasets in versions without noise and with added Gaussian noise. The zero and
first persistent homologies for the embeddings of the formed neural networks on each layer were calculated. To assess
the quality of classification, the accuracy metric was used. It is shown that the barcodes for embeddings on each layer of
the Bayesianized neural network in all four scenarios are between the corresponding barcodes of the deterministic and
Bayesian neural networks for both zero and first persistent homologies. In this case, the deterministic neural network is
the lower bound, and the Bayesian neural network is the upper bound. It is shown that the structure of data associations
within a Bayesianized neural network is inherited from a deterministic model, but acquires the properties of a Bayesian
one. It has been experimentally established that there is a relationship between the normalized persistent entropy
calculated on neural network embeddings and the accuracy of the neural network. For predicting accuracy, the topology
of embeddings on the middle layer of the neural network model turned out to be the most revealing. The proposed
approach can be used to simplify the construction of a Bayesian neural network from an already trained deterministic
neural network, which opens up the possibility of increasing the accuracy of an existing neural network without ensemble
with additional classifiers. It becomes possible to proactively evaluate the effectiveness of the generated neural network
on simplified data without running it on a real dataset, which reduces the resource intensity of its development
Path homologies of deep feedforward networks
We provide a characterization of two types of directed homology for
fully-connected, feedforward neural network architectures. These exact
characterizations of the directed homology structure of a neural network
architecture are the first of their kind. We show that the directed flag
homology of deep networks reduces to computing the simplicial homology of the
underlying undirected graph, which is explicitly given by Euler characteristic
computations. We also show that the path homology of these networks is
non-trivial in higher dimensions and depends on the number and size of the
layers within the network. These results provide a foundation for investigating
homological differences between neural network architectures and their realized
structure as implied by their parameters.Comment: To appear in the proceedings of IEEE ICMLA 201