1
|
Qin C, Zheng B, Zeng J, Chen Z, Zhai Y, Genovese A, Piuri V, Scotti F. Dynamically aggregating MLPs and CNNs for skin lesion segmentation with geometry regularization. Comput Methods Programs Biomed 2023; 238:107601. [PMID: 37210926 DOI: 10.1016/j.cmpb.2023.107601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 04/24/2023] [Accepted: 05/13/2023] [Indexed: 05/23/2023]
Abstract
BACKGROUND AND OBJECTIVE Melanoma is a highly malignant skin tumor. Accurate segmentation of skin lesions from dermoscopy images is pivotal for computer-aided diagnosis of melanoma. However, blurred lesion boundaries, variable lesion shapes, and other interference factors pose a challenge in this regard. METHODS This work proposes a novel framework called CFF-Net (Cross Feature Fusion Network) for supervised skin lesion segmentation. The encoder of the network includes dual branches, where the CNNs branch aims to extract rich local features while MLPs branch is used to establish both the global-spatial-dependencies and global-channel-dependencies for precise delineation of skin lesions. Besides, a feature-interaction module between two branches is designed for strengthening the feature representation by allowing dynamic exchange of spatial and channel information, so as to retain more spatial details and inhibit irrelevant noise. Moreover, an auxiliary prediction task is introduced to learn the global geometric information, highlighting the boundary of the skin lesion. RESULTS Comprehensive experiments using four publicly available skin lesion datasets (i.e., ISIC 2018, ISIC 2017, ISIC 2016, and PH2) indicated that CFF-Net outperformed the state-of-the-art models. In particular, CFF-Net greatly increased the average Jaccard Index score from 79.71% to 81.86% in ISIC 2018, from 78.03% to 80.21% in ISIC 2017, from 82.58% to 85.38% in ISIC 2016, and from 84.18% to 89.71% in PH2 compared with U-Net. Ablation studies demonstrated the effectiveness of each proposed component. Cross-validation experiments in ISIC 2018 and PH2 datasets verified the generalizability of CFF-Net under different skin lesion data distributions. Finally, comparison experiments using three public datasets demonstrated the superior performance of our model. CONCLUSION The proposed CFF-Net performed well in four public skin lesion datasets, especially for challenging cases with blurred edges of skin lesions and low contrast between skin lesions and background. CFF-Net can be employed for other segmentation tasks with better prediction and more accurate delineation of boundaries.
Collapse
Affiliation(s)
- Chuanbo Qin
- Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen 529020, China
| | - Bin Zheng
- Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen 529020, China
| | - Junying Zeng
- Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen 529020, China.
| | - Zhuyuan Chen
- Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen 529020, China
| | - Yikui Zhai
- Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen 529020, China
| | - Angelo Genovese
- Departimento di Information, Università degli Studi di Milano, 20133 Milano, Italy
| | - Vincenzo Piuri
- Departimento di Information, Università degli Studi di Milano, 20133 Milano, Italy
| | - Fabio Scotti
- Departimento di Information, Università degli Studi di Milano, 20133 Milano, Italy
| |
Collapse
|
2
|
|
3
|
Varadarajan V, Kommers P, Piuri V, Subramaniyaswamy V. Recent trends, challenges and applications in cognitive computing for intelligent systems. IFS 2020. [DOI: 10.3233/jifs-189309] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Vijayakumar Varadarajan
- School of Computer Science and Engineering, University of New South Wales, Sydney, Australia
| | | | - Vincenzo Piuri
- Dipartimento di Informatica, Universita’ degli Studi di Milano, Milano, Italy
| | | |
Collapse
|
4
|
|
5
|
Donida Labati R, Genovese A, Muñoz E, Piuri V, Scotti F. A novel pore extraction method for heterogeneous fingerprint images using Convolutional Neural Networks. Pattern Recognit Lett 2018. [DOI: 10.1016/j.patrec.2017.04.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
|
6
|
Donida Labati R, Genovese A, Muñoz E, Piuri V, Scotti F, Sforza G. Computational Intelligence for Biometric Applications: a Survey. ACTA ACUST UNITED AC 2016. [DOI: 10.47839/ijc.15.1.829] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Biometric systems consist of devices, procedures, and algorithms used to recognize people based on their physiological or behavioral features, known as biometric traits. Computational intelligence (CI) approaches are widely adopted in establishing identity based on biometrics and also to overcome non-idealities typically present in the samples. Typical areas include sample enhancement, feature extraction, classification, indexing, fusion, normalization, and anti-spoofing. In this context, computational intelligence plays an important role in performing of complex non-linear computations by creating models from the training data. These approaches are based on supervised as well as unsupervised training techniques. This work presents computational intelligence techniques applied to biometrics, from both a theoretical and an application point of view.
Collapse
|
7
|
Yassine A, Shirmohammadi S, Hu Y, Piuri V. IEEE CIVEMSA 2015-Computational Intelligence and Virtual Environments for Measurement Systems and Applications [Conference Reports]. IEEE COMPUT INTELL M 2015. [DOI: 10.1109/mci.2015.2472116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
|
8
|
Yassine A, Shirmohammadi S, Piuri V. IEEE CIVEMSA 2014 - Computational Intelligence and Virtual Environments for Measurement Systems and Applications [Conference Reports]. IEEE COMPUT INTELL M 2014. [DOI: 10.1109/mci.2014.2326097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
|
9
|
Bellocchio F, Ferrari S, Piuri V, Borghese NA. Hierarchical approach for multiscale support vector regression. IEEE Trans Neural Netw Learn Syst 2012; 23:1448-1460. [PMID: 24807928 DOI: 10.1109/tnnls.2012.2205018] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Support vector regression (SVR) is based on a linear combination of displaced replicas of the same function, called a kernel. When the function to be approximated is nonstationary, the single kernel approach may be ineffective, as it is not able to follow the variations in the frequency content in the different regions of the input space. The hierarchical support vector regression (HSVR) model presented here aims to provide a good solution also in these cases. HSVR consists of a set of hierarchical layers, each containing a standard SVR with Gaussian kernel at a given scale. Decreasing the scale layer by layer, details are incorporated inside the regression function. HSVR has been widely applied to noisy synthetic and real datasets and it has shown the ability in denoising the original data, obtaining an effective multiscale reconstruction of better quality than that obtained by standard SVR. Results also compare favorably with multikernel approaches. Furthermore, tuning the SVR configuration parameters is strongly simplified in the HSVR model.
Collapse
|
10
|
|
11
|
|
12
|
Abstract
In this paper, a novel real-time online network model is presented. It is derived from the hierarchical radial basis function (HRBF) model and it grows by automatically adding units at smaller scales, where the surface details are located, while data points are being collected. Real-time operation is achieved by exploiting the quasi-local nature of the Gaussian units: through the definition of a quad-tree structure to support their receptive field local network reconfiguration can be obtained. The model has been applied to 3-D scanning, where an updated real-time display of the manifold to the operator is fundamental to drive the acquisition procedure itself. Quantitative results are reported, which show that the accuracy achieved is comparable to that of two batch approaches: batch HRBF and support vector machines (SVMs). However, these two approaches are not suitable to real-time online learning. Moreover, proof of convergence is also given.
Collapse
Affiliation(s)
- Stefano Ferrari
- Department of Information Technology, Università degli Studi di Milano, Crema, Italy.
| | | | | | | |
Collapse
|
13
|
|
14
|
Piuri V. 2009 IEEE Symposium Series on Computational Intelligence (SSCI 2009) [Conference Reports. IEEE COMPUT INTELL M 2009. [DOI: 10.1109/mci.2009.934632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
|
15
|
Citterio C, Pelagotti A, Piuri V, Rocca L. Function approximation--a fast-convergence neural approach based on spectral analysis. IEEE Trans Neural Netw 2008; 10:725-40. [PMID: 18252573 DOI: 10.1109/72.774207] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
We propose a constructive approach to building single-hidden-layer neural networks for nonlinear function approximation using frequency domain analysis. We introduce a spectrum-based learning procedure that minimizes the difference between the spectrum of the training data and the spectrum of the network's estimates. The network is built up incrementally during training and automatically determines the appropriate number of hidden units. This technique achieves similar or better approximation with faster convergence times than traditional techniques such as backpropagation.
Collapse
Affiliation(s)
- C Citterio
- Foster Wheeler Italiana S.p.A., 20094 Milano, Italy
| | | | | | | |
Collapse
|
16
|
|
17
|
Piuri V. A Look to Our Past with Our Future in Mind [President's Message]. IEEE COMPUT INTELL M 2007. [DOI: 10.1109/mci.2007.910200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
|
18
|
Piuri V. Growing Together [President's Message]. IEEE COMPUT INTELL M 2007. [DOI: 10.1109/mci.2007.385356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
|
19
|
Piuri V. Educational Activities [President's Message]. IEEE COMPUT INTELL M 2007. [DOI: 10.1109/mci.2007.357172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
|
20
|
Piuri V. President's message - Serving your needs... IEEE COMPUT INTELL M 2007. [DOI: 10.1109/mci.2007.353414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
|
21
|
Piuri V. A Look to Our Past with Our Future in Mind [President's Message]. IEEE COMPUT INTELL M 2007. [DOI: 10.1109/mci.2007.4313026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
|
22
|
Abstract
Modern scanners are able to deliver huge quantities of three-dimensional (3-D) data points sampled on an object's surface, in a short time. These data have to be filtered and their cardinality reduced to come up with a mesh manageable at interactive rates. We introduce here a novel procedure to accomplish these two tasks, which is based on an optimized version of soft vector quantization (VQ). The resulting technique has been termed enhanced vector quantization (EVQ) since it introduces several improvements with respect to the classical soft VQ approaches. These are based on computationally expensive iterative optimization; local computation is introduced here, by means of an adequate partitioning of the data space called hyperbox (HB), to reduce the computational time so as to be linear in the number of data points N, saving more than 80% of time in real applications. Moreover, the algorithm can be fully parallelized, thus leading to an implementation that is sublinear in N. The voxel side and the other parameters are automatically determined from data distribution on the basis of the Zador's criterion. This makes the algorithm completely automatic. Because the only parameter to be specified is the compression rate, the procedure is suitable even for nontrained users. Results obtained in reconstructing faces of both humans and puppets as well as artifacts from point clouds publicly available on the web are reported and discussed, in comparison with other methods available in the literature. EVQ has been conceived as a general procedure, suited for VQ applications with large data sets whose data space has relatively low dimensionality.
Collapse
Affiliation(s)
- Stefano Ferrari
- Department of Information Technologies, University of Milano, Crema (CR) 26013, Italy.
| | | | | | | |
Collapse
|
23
|
Piuri V. IEEE/CIS Publications Activities [President's Message]. IEEE COMPUT INTELL M 2006. [DOI: 10.1109/mci.2006.329684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
|
24
|
Piuri V. IEEE/CIS Publications Activities. IEEE COMPUT INTELL M 2006. [DOI: 10.1109/ci-m.2006.248040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
|
25
|
Piuri V. CIS: Conference activities. IEEE COMPUT INTELL M 2006. [DOI: 10.1109/mci.2006.1672980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
|
26
|
Piuri V. President's message - Cis: technically speaking. IEEE COMPUT INTELL M 2006. [DOI: 10.1109/mci.2006.1626488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
|
27
|
Piuri V. Welcome to IEEE Computational Intelligence Magazine! IEEE COMPUT INTELL M 2006. [DOI: 10.1109/mci.2006.1597054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
|
28
|
Alippi C, de Russis C, Piuri V. A neural-network based control solution to air-fuel ratio control for automotive fuel-injection systems. ACTA ACUST UNITED AC 2003. [DOI: 10.1109/tsmcc.2003.814035] [Citation(s) in RCA: 44] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
|
29
|
|
30
|
Abstract
This paper proposes a design methodology of information technology architectures tying organizational requirements to technical choices and costs. The primary goal is to provide a structured support for the selection of the minimum-cost architecture satisfying given organizational requirements. Previous empirical studies have attempted absolute cost comparisons of different architectural solutions, primarily relying on the expertise of practitioners and a priori beliefs, but have rarely taken into account the impact of organizational requirements on costs. Requirements are modelled as information processes, composed of tasks exchanging information and characterized by varying levels of computational complexity. Different architectural distributions of presentation, computation and data management applications are compared. The cost implications of organizational requirements for processing intensity, communication intensity and networking are analysed. The results show a relationship between structural features of information processes and architectural costs and indicate how architectural design should be based on organizational as well as technology considerations.
Collapse
|
31
|
Pelagotti A, Piuri V. Entropic analysis and incremental synthesis of multilayered feedforward neural networks. Int J Neural Syst 1997; 8:647-59. [PMID: 10065841 DOI: 10.1142/s0129065797000574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Neural network architecture optimization is often a critical issue, particularly when VLSI implementation is considered. This paper proposes a new minimization method for multilayered feedforward ANNs and an original approach to their synthesis, both based on the analysis of the information quantity (entropy) flowing through the network. A layer is described as an information filter which selects the relevant characteristics until the complete classification is performed. The basic incremental synthesis method, including the supervised training procedure, is derived to design application-tailored neural paradigms with good generalization capability.
Collapse
Affiliation(s)
- A Pelagotti
- Department of Electronics and Information, Politecnico di Milano, Italy
| | | |
Collapse
|
32
|
|