Here you can find a consolidated (a.k.a. slowly updated) list of my publications. A frequently updated (and possibly noisy) list of works is available on my Google Scholar profile.
Please find below a short list of highlight publications for my recent activity.
Ferrari, Elisa; Retico, Alessandra; Bacciu, Davide Measuring the effects of confounders in medical supervised classification problems: the Confounding Index (CI) Journal Article In: Artificial Intelligence in Medicine, vol. 103, 2020. H, Jarman Ian; A, Etchells Terence; Davide, Bacciu; M, Garibaldi John; O, Ellis Ian; JG, Lisboa Paulo Clustering of protein expression data: a benchmark of statistical and neural approaches Journal Article In: Soft Computing-A Fusion of Foundations, Methodologies and Applications, vol. 15, no. 8, pp. 1459–1469, 2011, ISSN: 1432-7643. S, Fernandes Ana; Davide, Bacciu; H, Jarman Ian; A, Etchells Terence; M, Fonseca Jose; JG, Lisboa Paulo Different Methodologies for Patient Stratification Using Survival Data Conference Lecture Notes in Computer ScienceComputational Intelligence Methods for Bioinformatics and Biostatistics, vol. 6160, 2010. Davide, Bacciu; Antonina, Starita Expansive competitive learning for kernel vector quantization Journal Article In: Pattern Recognition Letters, vol. 30, no. 6, pp. 641–651, 2009, ISSN: 0167-8655. Davide, Bacciu; H, Jarman Ian; A, Etchells Terence; G, Lisboa Paulo J Patient stratification with competing risks by multivariate Fisher distance Conference 2009 International Joint Conference on Neural Networks, IEEE, 2009. Davide, Bacciu A Perceptual Learning Model to Discover the Hierarchical Latent Structure of Image Collections PhD Thesis 2008. Davide, BACCIU; Elia, BIGANZOLI; JG, LISBOA Paulo; Antonina, Starita Are Model-based Clustering and Neural Clustering Consistent? A Case Study from Bioinformatics Conference Proceedings of the 12th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES'08), vol. 5178, Springer, 2008. Davide, Bacciu; Antonina, Starita Convergence Behavior of Competitive Repetition-Suppression Clustering Conference Neural Information Processing, Lecture Notes in Computer Science, vol. 4984, Springer, 2008. Davide, Bacciu; Antonina, Starita A Robust Bio-Inspired Clustering Algorithm for the Automatic Determination of Unknown Cluster Number Conference 2007 International Joint Conference on Neural Networks, IEEE, 2007.
@article{aime20Confound,
title = {Measuring the effects of confounders in medical supervised classification problems: the Confounding Index (CI)},
author = {Elisa Ferrari and Alessandra Retico and Davide Bacciu},
url = {https://arxiv.org/abs/1905.08871},
doi = {10.1016/j.artmed.2020.101804},
year = {2020},
date = {2020-03-01},
journal = {Artificial Intelligence in Medicine},
volume = {103},
abstract = {Over the years, there has been growing interest in using Machine Learning techniques for biomedical data processing. When tackling these tasks, one needs to bear in mind that biomedical data depends on a variety of characteristics, such as demographic aspects (age, gender, etc) or the acquisition technology, which might be unrelated with the target of the analysis. In supervised tasks, failing to match the ground truth targets with respect to such characteristics, called confounders, may lead to very misleading estimates of the predictive performance. Many strategies have been proposed to handle confounders, ranging from data selection, to normalization techniques, up to the use of training algorithm for learning with imbalanced data. However, all these solutions require the confounders to be known a priori. To this aim, we introduce a novel index that is able to measure the confounding effect of a data attribute in a bias-agnostic way. This index can be used to quantitatively compare the confounding effects of different variables and to inform correction methods such as normalization procedures or ad-hoc-prepared learning algorithms. The effectiveness of this index is validated on both simulated data and real-world neuroimaging data. },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{soco2011,
title = {Clustering of protein expression data: a benchmark of statistical and neural approaches},
author = {Jarman Ian H and Etchells Terence A and Bacciu Davide and Garibaldi John M and Ellis Ian O and Lisboa Paulo JG},
url = {http://dx.doi.org/10.1007/s00500-010-0596-9},
doi = {10.1007/s00500-010-0596-9},
issn = {1432-7643},
year = {2011},
date = {2011-01-01},
journal = {Soft Computing-A Fusion of Foundations, Methodologies and Applications},
volume = {15},
number = {8},
pages = {1459--1469},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@conference{11568_465483,
title = {Different Methodologies for Patient Stratification Using Survival Data},
author = {Fernandes Ana S and Bacciu Davide and Jarman Ian H and Etchells Terence A and Fonseca Jose M and Lisboa Paulo JG},
doi = {10.1007/978-3-642-14571-1_21},
year = {2010},
date = {2010-01-01},
booktitle = {Lecture Notes in Computer ScienceComputational Intelligence Methods for Bioinformatics and Biostatistics},
journal = {LECTURE NOTES IN COMPUTER SCIENCE},
volume = {6160},
pages = {276--290},
abstract = {Clinical characterization of breast cancer patients related to their risk and profiles is an important part for making their correct prognostic assessments. This paper first proposes a prognostic index obtained when it is applied a flexible non-linear time-to-event model and compares it to a widely used linear survival estimator. This index underpins different stratification methodologies including informed clustering utilising the principle of learning metrics, regression trees and recursive application of the log-rank test. Missing data issue was overcome using multiple imputation, which was applied to a neural network model of survival fitted to a data set for breast cancer (n=743). It was found the three methodologies broadly agree, having however important differences.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@article{patrec2009,
title = {Expansive competitive learning for kernel vector quantization},
author = {Bacciu Davide and Starita Antonina},
url = {http://dx.doi.org/10.1016/j.patrec.2009.01.002},
doi = {10.1016/j.patrec.2009.01.002},
issn = {0167-8655},
year = {2009},
date = {2009-01-01},
journal = {Pattern Recognition Letters},
volume = {30},
number = {6},
pages = {641--651},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@conference{11568_465484,
title = {Patient stratification with competing risks by multivariate Fisher distance},
author = {Bacciu Davide and Jarman Ian H and Etchells Terence A and Lisboa Paulo J G},
doi = {10.1109/IJCNN.2009.5179077},
year = {2009},
date = {2009-01-01},
urldate = {2009-01-01},
booktitle = {2009 International Joint Conference on Neural Networks},
pages = {3453--3460},
publisher = {IEEE},
abstract = {Early characterization of patients with respect to their predicted response to treatment is a fundamental step towards the delivery of effective, personalized care. Starting from the results of a time-to-event model with competing risks using the framework of partial logistic artificial neural networks with automatic relevance determination (PLANNCR-ARD), we discuss an effective semi-supervised approach to patient stratification with application to Acute Myeloid Leukaemia (AML) data (n = 509) acquired prospectively by the GIMEMA consortium. Multiple prognostic indices provided by the survival model are exploited to build a metric based on the Fisher information matrix. Cluster number estimation is then performed in the Fisher-induced affine space, yielding to the discovery of a stratification of the patients into groups characterized by significantly different mortality risks following induction therapy in AML. The proposed model is shown to be able to cluster the input data, while promoting specificity of both target outcomes, namely Complete Remission (CR) and Induction Death (ID). This generic clustering methodology generates an affine transformation of the data space that is coherent with the prognostic information predicted by the PLANNCR-ARD model.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@phdthesis{11568_466874,
title = {A Perceptual Learning Model to Discover the Hierarchical Latent Structure of Image Collections},
author = { Bacciu Davide},
url = {http://e-theses.imtlucca.it/id/eprint/7},
doi = {10.6092/imtlucca/e-theses/7},
year = {2008},
date = {2008-01-01},
urldate = {2008-01-01},
publisher = {IMT Lucca},
abstract = {Biology has been an unparalleled source of inspiration for the work of researchers in several scientific and engineering fields including computer vision. The starting point of this thesis is the neurophysiological properties of the human early visual system, in particular, the cortical mechanism that mediates learning by exploiting information about stimuli repetition. Repetition has long been considered a fundamental correlate of skill acquisition andmemory formation in biological aswell as computational learning models. However, recent studies have shown that biological neural networks have differentways of exploiting repetition in forming memory maps. The thesis focuses on a perceptual learning mechanism called repetition suppression, which exploits the temporal distribution of neural activations to drive an efficient neural allocation for a set of stimuli. This explores the neurophysiological hypothesis that repetition suppression serves as an unsupervised perceptual learning mechanism that can drive efficient memory formation by reducing the overall size of stimuli representation while strengthening the responses of the most selective neurons. This interpretation of repetition is different from its traditional role in computational learning models mainly to induce convergence and reach training stability, without using this information to provide focus for the neural representations of the data. The first part of the thesis introduces a novel computational model with repetition suppression, which forms an unsupervised competitive systemtermed CoRe, for Competitive Repetition-suppression learning. The model is applied to generalproblems in the fields of computational intelligence and machine learning. Particular emphasis is placed on validating the model as an effective tool for the unsupervised exploration of bio-medical data. In particular, it is shown that the repetition suppression mechanism efficiently addresses the issues of automatically estimating the number of clusters within the data, as well as filtering noise and irrelevant input components in highly dimensional data, e.g. gene expression levels from DNA Microarrays. The CoRe model produces relevance estimates for the each covariate which is useful, for instance, to discover the best discriminating bio-markers. The description of the model includes a theoretical analysis using Huber’s robust statistics to show that the model is robust to outliers and noise in the data. The convergence properties of themodel also studied. It is shown that, besides its biological underpinning, the CoRe model has useful properties in terms of asymptotic behavior. By exploiting a kernel-based formulation for the CoRe learning error, a theoretically sound motivation is provided for the model’s ability to avoid local minima of its loss function. To do this a necessary and sufficient condition for global error minimization in vector quantization is generalized by extending it to distance metrics in generic Hilbert spaces. This leads to the derivation of a family of kernel-based algorithms that address the local minima issue of unsupervised vector quantization in a principled way. The experimental results show that the algorithm can achieve a consistent performance gain compared with state-of-the-art learning vector quantizers, while retaining a lower computational complexity (linear with respect to the dataset size). Bridging the gap between the low level representation of the visual content and the underlying high-level semantics is a major research issue of current interest. The second part of the thesis focuses on this problem by introducing a hierarchical and multi-resolution approach to visual content understanding. On a spatial level, CoRe learning is used to pool together the local visual patches by organizing them into perceptually meaningful intermediate structures. On the semantical level, it provides an extension of the probabilistic Latent Semantic Analysis (pLSA) model that allows discovery and organization of the visual topics into a hierarchy of aspects. The proposed hierarchical pLSA model is shown to effectively address the unsupervised discovery of relevant visual classes from pictorial collections, at the same time learning to segment the image regions containing the discovered classes. Furthermore, by drawing on a recent pLSA-based image annotation system, the hierarchical pLSA model is extended to process and representmulti-modal collections comprising textual and visual data. The results of the experimental evaluation show that the proposed model learns to attach textual labels (available only at the level of the whole image) to the discovered image regions, while increasing the precision/ recall performance with respect to flat, pLSA annotation model.},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
@conference{11568_465487,
title = {Are Model-based Clustering and Neural Clustering Consistent? A Case Study from Bioinformatics},
author = {BACCIU Davide and BIGANZOLI Elia and LISBOA Paulo JG and Starita Antonina},
doi = {10.1007/978-3-540-85565-1-23},
year = {2008},
date = {2008-01-01},
booktitle = {Proceedings of the 12th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES'08)},
journal = {LECTURE NOTES IN COMPUTER SCIENCE},
volume = {5178},
pages = {181--188},
publisher = {Springer},
abstract = {A novel neural network clustering algorithm, CoRe, is benchmarked against previously published results on a breast cancer data set and applying the method of Partition Around Medoids (PAM). The data serve to compare the samples partitions obtained with the neural network, PAM and model-based algorithms, namely Gaussian Mixture Model (GMM), Variational Bayesian Gaussian Mixture (VBG) and Variational Bayesian Mixtures with Splitting (VBS). It is found that CoRe, on the one hand, agrees with the previously published partitions; on the other hand, it supports the existence of a supplementary cluster that we hypothesize to be an additional tumor subgroup with respect to those previously identified by PAM},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{11568_466667,
title = {Convergence Behavior of Competitive Repetition-Suppression Clustering},
author = {Bacciu Davide and Starita Antonina },
doi = {10.1007/978-3-540-69158-7_52},
year = {2008},
date = {2008-01-01},
booktitle = {Neural Information Processing, Lecture Notes in Computer Science},
volume = {4984},
pages = {497--506},
publisher = {Springer},
abstract = {Competitive Repetition-suppression (CoRe) clustering is a bio-inspired learning algorithm that is capable of automatically determining the unknown cluster number from the data. In a previous work it has been shown how CoRe clustering represents a robust generalization of rival penalized competitive learning (RPCL) by means of M-estimators. This paper studies the convergence behavior of the CoRe model, based on the analysis proposed for the distance-sensitive RPCL (DSRPCL) algorithm. Furthermore, it is proposed a global minimum criterion for learning vector quantization in kernel space that is used to assess the correct location property for the CoRe algorithm.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{11568_466670,
title = {A Robust Bio-Inspired Clustering Algorithm for the Automatic Determination of Unknown Cluster Number},
author = {Bacciu Davide and Starita Antonina },
doi = {10.1109/IJCNN.2007.4371148},
year = {2007},
date = {2007-01-01},
urldate = {2007-01-01},
booktitle = {2007 International Joint Conference on Neural Networks},
pages = {1314--1319},
publisher = {IEEE},
abstract = {The paper introduces a robust clustering algorithm that can automatically determine the unknown cluster number from noisy data without any a-priori information. We show how our clustering algorithm can be derived from a general learning theory, named CoRe learning, that models a cortical memory mechanism called repetition suppression. Moreover, we describe CoRe clustering relationships with Rival Penalized Competitive Learning (RPCL), showing how CoRe extends this model by strengthening the rival penalization estimation by means of robust loss functions. Finally, we present the results of simulations concerning the unsupervised segmentation of noisy images.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}