12 research outputs found
Persistence codebooks for topological data analysis
Persistent homology is a rigorous mathematical theory that provides a robust descriptor of data in the form of persistence diagrams (PDs) which are 2D multisets of points. Their variable size makes them, however, difficult to combine with typical machine learning workflows. In this paper we introduce persistence codebooks, a novel expressive and discriminative fixed-size vectorized representation of PDs that adapts to the inherent sparsity of persistence diagrams. To this end, we adapt bag-of-words, vectors of locally aggregated descriptors and Fischer vectors for the quantization of PDs. Persistence codebooks represent PDs in a convenient way for machine learning and statistical analysis and have a number of favorable practical and theoretical properties including 1-Wasserstein stability. We evaluate the presented representations on several heterogeneous datasets and show their (high) discriminative power. Our approach yields comparable-and partly even higher-performance in much less time than alternative approaches
Towards personalized diagnosis of Glioblastoma in Fluid-attenuated inversion recovery (FLAIR) by topological interpretable machine learning
Glioblastoma multiforme (GBM) is a fast-growing and highly invasive brain
tumour, it tends to occur in adults between the ages of 45 and 70 and it
accounts for 52 percent of all primary brain tumours. Usually, GBMs are
detected by magnetic resonance images (MRI). Among MRI, Fluid-attenuated
inversion recovery (FLAIR) sequence produces high quality digital tumour
representation. Fast detection and segmentation techniques are needed for
overcoming subjective medical doctors (MDs) judgment. In the present
investigation, we intend to demonstrate by means of numerical experiments that
topological features combined with textural features can be enrolled for GBM
analysis and morphological characterization on FLAIR. To this extent, we have
performed three numerical experiments. In the first experiment, Topological
Data Analysis (TDA) of a simplified 2D tumour growth mathematical model had
allowed to understand the bio-chemical conditions that facilitate tumour
growth: the higher the concentration of chemical nutrients the more virulent
the process. In the second experiment topological data analysis was used for
evaluating GBM temporal progression on FLAIR recorded within 90 days following
treatment (e.g., chemo-radiation therapy - CRT) completion and at progression.
The experiment had confirmed that persistent entropy is a viable statistics for
monitoring GBM evolution during the follow-up period. In the third experiment
we had developed a novel methodology based on topological and textural features
and automatic interpretable machine learning for automatic GBM classification
on FLAIR. The algorithm reached a classification accuracy up to the 97%.Comment: 22 pages; 16 figure
Mathematics in Medical Diagnostics - 2022 Proceedings of the 4th International Conference on Trauma Surgery Technology
The 4th event of the Giessen International Conference Series on Trauma Surgery Technology took place on April, the 23rd 2022 in Warsaw, Poland. It aims to bring together practical application research, with a focus on medical imaging, and the TDA experts from Warsaw. This publication contains details of our presentations and discussions
Approximating Continuous Functions on Persistence Diagrams Using Template Functions
The persistence diagram is an increasingly useful tool from Topological Data
Analysis, but its use alongside typical machine learning techniques requires
mathematical finesse. The most success to date has come from methods that map
persistence diagrams into , in a way which maximizes the
structure preserved. This process is commonly referred to as featurization. In
this paper, we describe a mathematical framework for featurization using
template functions. These functions are general as they are only required to be
continuous and compactly supported. We discuss two realizations: tent
functions, which emphasize the local contributions of points in a persistence
diagram, and interpolating polynomials, which capture global pairwise
interactions. We combine the resulting features with classification and
regression algorithms on several examples including shape data and the Rossler
system. Our results show that using template functions yields high accuracy
rates that match and often exceed those of existing featurization methods. One
counter-intuitive observation is that in most cases using interpolating
polynomials, where each point contributes globally to the feature vector,
yields significantly better results than using tent functions, where the
contribution of each point is localized. Along the way, we provide a complete
characterization of compactness in the space of persistence diagrams