1,721,157 research outputs found
An automatic system to discriminate malignant from benign massive lesions on mammograms
No full textMammography is widely recognized as the most reliable technique for early detection of breast cancers. Automated or semi-automated computerized classification schemes can be very useful in assisting radiologists with a second opinion about the visual diagnosis of breast lesions, thus leading to a reduction in the number of unnecessary biopsies. We present a computer-aided diagnosis (CADi) system for the characterization of massive lesions in mammograms, whose aim is to distinguish malignant from benign masses. The CADi system we realized is based on a three-stage algorithm: (a) a segmentation technique extracts the contours of the massive lesion from the image; (b) 16 features based on size and shape of the lesion are computed; (c) a neural classifier merges the features into an estimated likelihood of malignancy. A data set of 226 massive lesions (109 malignant and 117 benign) has been used in this study. The system performances have been evaluated in terms of the receiver-operating characteristic (ROC) analysis, obtaining A(z) = 0.80 +/- 0.04 as the estimated area under the ROC curve. (c) 2006 Elsevier B.V. All rights reserved. RI Retico, Alessandra /I-6321-201
A view into the brain of female children with autism pectrum disorder: Morphometric regional alterations detected by structural MRI mass-univariate and pattern classification analisys
No full textFemale children with autism spectrum disorder: An insight from mass-univariate
and pattern classification analyses.
Calderoni S, Retico A, Biagi L, Tancredi R, Muratori F, Tosetti M.
IRCCS Stella Maris Foundation, Viale del Tirreno 331, 56018 Calambrone, Pisa,
Italy; Division of Child Neurology and Psychiatry, University of Pisa, Pisa,
Italy.
Several studies on structural MRI in children with autism spectrum disorders
(ASD) have mainly focused on samples prevailingly consisting of males. Sex
differences in brain structure are observable since infancy and therefore caution
is required in transferring to females the results obtained for males. The
neuroanatomical phenotype of female children with ASD (ASDf) represents indeed a
neglected area of research. In this study, we investigated for the first time the
anatomic brain structures of a sample entirely composed of ASDf (n=38; 2-7years
of age; mean=53months; SD=18) with respect to 38 female age and non verbal IQ
matched controls, using both mass-univariate and pattern classification
approaches. The whole brain volumes of each group were compared using voxel-based
morphometry (VBM) with diffeomorphic anatomical registration through
exponentiated lie algebra (DARTEL) procedure, allowing us to build a
study-specific template. Significantly more gray matter (GM) was found in the
left superior frontal gyrus (SFG) in ASDf subjects compared to controls. The GM
segments obtained in the VBM-DARTEL preprocessing are also classified with a
support vector machine (SVM), using the leave-pair-out cross-validation protocol.
Then, the recursive feature elimination (SVM-RFE) approach allows for the
identification of the most discriminating voxels in the GM segments and these
prove extremely consistent with the SFG region identified by the VBM analysis.
Furthermore, the SVM-RFE map obtained with the most discriminating set of voxels
corresponding to the maximum Area Under the Receiver Operating Characteristic
Curve (AUC(max)=0.80) highlighted a more complex circuitry of increased cortical
volume in ASDf, involving bilaterally the SFG and the right temporo-parietal
junction (TPJ). The SFG and TPJ abnormalities may be relevant to the
pathophysiology of ASDf, since these structures participate in some core atypical
features of autism
Breast density characterization through convolutional neural networks and first order statistical features
No full textA Computer-Aided Detection system for lung nodules in CT images
No full textLung cancer is the leading cause of cancer-related mortality in developed countries. To support radiologists in the identification of early-stage lung cancers, we propose a Computer Aided Detection (CAD) system, composed by two different procedures: VBNACADI devoted to the identification of small nodules embedded in the lung parenchyma (internal nodules) and VBNACADJP devoted the identification of nodules originating on the pleura surface (juxta-pleural nod- ules). The CAD system has been developed and tested on a dataset of low-dose and thin-slice CT scans collected in the framework of the first Italian randomized and controlled screening trial (ITALUNG-CT). This work has been carried out in the framework of MAGIC-5 (Medical Application on a Grid Infrastructure Connection) Italian collaboration funded by Istituto Nazionale di Fisica Nucleare (INFN) and Ministero dell’Universit`a e della Ricerca (MIUR), which aims at developing models and algorithms for a distributed analysis of biomedical images, by making use of the GRID services
An automatic system to discriminate malignant from benign massive lesions in mammograms
No full textEvaluating the degree of malignancy of a massive lesion on the basis of the mere visual analysis of the mammogram is a non-trivial task. We developed a semi-automated system for massive-lesion characterization with the aim to support the radiological diagnosis. A dataset of 226 masses has been used in the present analysis. The system performances have been evaluated in terms of the area under the ROC curve, obtaining Az= 0.80 ± 0.04
Multi-Site MRI Data Harmonization with an Adversarial Learning Approach: Implementation to the Study of Brain Connectivity in Autism Spectrum Disorders
No full textMagnetic resonance imaging (MRI) nowadays plays an important role in the identification of brain underpinnings in a wide range of neuropsychiatric disorders, including Autism Spectrum Disorders (ASD). Characterizing the hallmarks in these pathologies is not a straightforward task and machine learning (ML) is certainly one of the most promising tools for addressing complex and non-linear problems. ML algorithms and, in particular, deep neural networks (DNNs), need large datasets in order to be properly trained and thus ensure generalization capabilities on new data. Large datasets can be obtained by collecting images from different centers, thus bringing unavoidable biases in the analysis due to differences in hardware and scanning protocols between different centers. In this work, we dealt with the issue of multicenter MRI data harmonization by comparing two different approaches: the analytical ComBat-GAM procedure, whose effectiveness is already documented in the literature, and an originally developed site-adversarial deep neural network (ad-DNN). The latter aims to perform a classification task while simultaneously searching for site-relevant patterns in order to make predictions free from site-related biases. As a case study, we implemented DNN and ad-DNN classifiers to distinguish subjects with ASD with respect to typical developing controls based on functional connectivity measures derived from data of the multicenter ABIDE collection. The classification performance of the proposed ad-DNN, measured in terms of the area under the ROC curve (AUC), achieved the value of AUC = (Formula presented.), which is comparable to that obtained by a DNN on data harmonized according to the analytical procedure (AUC = (Formula presented.)). The relevant functional connectivity alterations identified by both procedures showed an agreement between each other and with the patterns of neuroanatomical alterations previously detected in the same cohort of subjects
Effect of data harmonization of multicentric dataset in ASD/TD classification
Full text linkMachine Learning (ML) is nowadays an essential tool in the analysis of Magnetic Resonance Imaging (MRI) data, in particular in the identification of brain correlates in neurological and neurodevelopmental disorders. ML requires datasets of appropriate size for training, which in neuroimaging are typically obtained collecting data from multiple acquisition centers. However, analyzing large multicentric datasets can introduce bias due to differences between acquisition centers. ComBat harmonization is commonly used to address batch effects, but it can lead to data leakage when the entire dataset is used to estimate model parameters. In this study, structural and functional MRI data from the Autism Brain Imaging Data Exchange (ABIDE) collection were used to classify subjects with Autism Spectrum Disorders (ASD) compared to Typical Developing controls (TD). We compared the classical approach (external harmonization) in which harmonization is performed before train/test split, with an harmonization calculated only on the train set (internal harmonization), and with the dataset with no harmonization. The results showed that harmonization using the whole dataset achieved higher discrimination performance, while non-harmonized data and harmonization using only the train set showed similar results, for both structural and connectivity features. We also showed that the higher performances of the external harmonization are not due to larger size of the sample for the estimation of the model and hence these improved performance with the entire dataset may be ascribed to data leakage. In order to prevent this leakage, it is recommended to define the harmonization model solely using the train set
An automatic system to discriminate malignant from benign massive lesions in mammograms
No full textEvaluating the degree of malignancy of a massive lesion on the basis of the mere visual analysis of the mammogram is a non-trivial task. We developed a semi-automated system for massive-lesion characterization with the aim to support the radiological diagnosis. A dataset of 226 masses has been used in the present analysis. The system performances have been evaluated in terms of the area under the ROC curve, obtaining Az= 0.80 ± 0.04
Exploring Autism Spectrum Disorder: A Comparative Study of Traditional Classifiers and Deep Learning Classifiers to Analyze Functional Connectivity Measures from a Multicenter Dataset
Full text linkThe investigation of functional magnetic resonance imaging (fMRI) data with traditional machine learning (ML) and deep learning (DL) classifiers has been widely used to study autism spectrum disorders (ASDs). This condition is characterized by symptoms that affect the individual’s behavioral aspects and social relationships. Early diagnosis is crucial for intervention, but the complexity of ASD poses challenges for the development of effective treatments. This study compares traditional ML and DL classifiers in the analysis of tabular data, in particular, functional connectivity measures obtained from the time series of a public multicenter dataset, and evaluates whether the features that contribute most to the classification task vary depending on the classifier used. Specifically, Support Vector Machine (SVM) classifiers, with both linear and radial basis function (RBF) kernels, and Extreme Gradient Boosting (XGBoost) classifiers are compared against the TabNet classifier (a DL architecture customized for tabular data analysis) and a Multi Layer Perceptron (MLP). The findings suggest that DL classifiers may not be optimal for the type of data analyzed, as their performance trails behind that of standard classifiers. Among the latter, SVMs outperform the other classifiers with an AUC of around 75%, whereas the best performances of TabNet and MLP reach 65% and 71% at most, respectively. Furthermore, the analysis of the feature importance showed that the brain regions that contribute the most to the classification task are those primarily responsible for sensory and spatial perception, as well as attention modulation, which is known to be altered in ASDs
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