14 research outputs found
Review of Machine Learning Methods for Additive Manufacturing of Functionally Graded Materials
Additive manufacturing has revolutionized the manufacturing of complex parts
by enabling direct material joining and offers several advantages such as
cost-effective manufacturing of complex parts, reducing manufacturing waste,
and opening new possibilities for manufacturing automation. One group of
materials for which additive manufacturing holds great potential for enhancing
component performance and properties is Functionally Graded Materials (FGMs).
FGMs are advanced composite materials that exhibit smoothly varying properties
making them desirable for applications in aerospace, automobile, biomedical,
and defense industries. Such composition differs from traditional composite
materials, since the location-dependent composition changes gradually in FGMs,
leading to enhanced properties. Recently, machine learning techniques have
emerged as a promising means for fabrication of FGMs through optimizing
processing parameters, improving product quality, and detecting manufacturing
defects. This paper first provides a brief literature review of works related
to FGM fabrication, followed by reviewing works on employing machine learning
in additive manufacturing, Afterward, we provide an overview of published works
in the literature related to the application of machine learning methods in
Directed Energy Deposition and for fabrication of FGMs.Comment: 11 page
Evaluation of Complexity Measures for Deep Learning Generalization in Medical Image Analysis
The generalization performance of deep learning models for medical image
analysis often decreases on images collected with different devices for data
acquisition, device settings, or patient population. A better understanding of
the generalization capacity on new images is crucial for clinicians'
trustworthiness in deep learning. Although significant research efforts have
been recently directed toward establishing generalization bounds and complexity
measures, still, there is often a significant discrepancy between the predicted
and actual generalization performance. As well, related large empirical studies
have been primarily based on validation with general-purpose image datasets.
This paper presents an empirical study that investigates the correlation
between 25 complexity measures and the generalization abilities of supervised
deep learning classifiers for breast ultrasound images. The results indicate
that PAC-Bayes flatness-based and path norm-based measures produce the most
consistent explanation for the combination of models and data. We also
investigate the use of multi-task classification and segmentation approach for
breast images, and report that such learning approach acts as an implicit
regularizer and is conducive toward improved generalization.Comment: 15 pages, 4 figure
Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review
Cancer is a term that denotes a group of diseases caused by the abnormal growth of cells that can spread in different parts of the body. According to the World Health Organization (WHO), cancer is the second major cause of death after cardiovascular diseases. Gene expression can play a fundamental role in the early detection of cancer, as it is indicative of the biochemical processes in tissue and cells, as well as the genetic characteristics of an organism. Deoxyribonucleic acid (DNA) microarrays and ribonucleic acid (RNA)-sequencing methods for gene expression data allow quantifying the expression levels of genes and produce valuable data for computational analysis. This study reviews recent progress in gene expression analysis for cancer classification using machine learning methods. Both conventional and deep learning-based approaches are reviewed, with an emphasis on the application of deep learning models due to their comparative advantages for identifying gene patterns that are distinctive for various types of cancers. Relevant works that employ the most commonly used deep neural network architectures are covered, including multi-layer perceptrons, as well as convolutional, recurrent, graph, and transformer networks. This survey also presents an overview of the data collection methods for gene expression analysis and lists important datasets that are commonly used for supervised machine learning for this task. Furthermore, we review pertinent techniques for feature engineering and data preprocessing that are typically used to handle the high dimensionality of gene expression data, caused by a large number of genes present in data samples. The paper concludes with a discussion of future research directions for machine learning-based gene expression analysis for cancer classification
Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review
Cancer is a term that denotes a group of diseases caused by the abnormal growth of cells that can spread in different parts of the body. According to the World Health Organization (WHO), cancer is the second major cause of death after cardiovascular diseases. Gene expression can play a fundamental role in the early detection of cancer, as it is indicative of the biochemical processes in tissue and cells, as well as the genetic characteristics of an organism. Deoxyribonucleic acid (DNA) microarrays and ribonucleic acid (RNA)-sequencing methods for gene expression data allow quantifying the expression levels of genes and produce valuable data for computational analysis. This study reviews recent progress in gene expression analysis for cancer classification using machine learning methods. Both conventional and deep learning-based approaches are reviewed, with an emphasis on the application of deep learning models due to their comparative advantages for identifying gene patterns that are distinctive for various types of cancers. Relevant works that employ the most commonly used deep neural network architectures are covered, including multi-layer perceptrons, as well as convolutional, recurrent, graph, and transformer networks. This survey also presents an overview of the data collection methods for gene expression analysis and lists important datasets that are commonly used for supervised machine learning for this task. Furthermore, we review pertinent techniques for feature engineering and data preprocessing that are typically used to handle the high dimensionality of gene expression data, caused by a large number of genes present in data samples. The paper concludes with a discussion of future research directions for machine learning-based gene expression analysis for cancer classification
BI-RADS-NET-V2: A Composite Multi-Task Neural Network for Computer-Aided Diagnosis of Breast Cancer in Ultrasound Images With Semantic and Quantitative Explanations
Computer-aided Diagnosis (CADx) based on explainable artificial intelligence (XAI) can gain the trust of radiologists and effectively improve diagnosis accuracy and consultation efficiency. This paper proposes BI-RADS-Net-V2, a novel machine learning approach for fully automatic breast cancer diagnosis in ultrasound images. The BI-RADS-Net-V2 can accurately distinguish malignant tumors from benign ones and provides both semantic and quantitative explanations. The explanations are provided in terms of clinically proven morphological features used by clinicians for diagnosis and reporting mass findings, i.e., Breast Imaging Reporting and Data System (BI-RADS). The experiments on 1,192 Breast Ultrasound (BUS) images indicate that the proposed method improves the diagnosis accuracy by taking full advantage of the medical knowledge in BI-RADS while providing both semantic and quantitative explanations for the decision
A Data Set of Human Body Movements for Physical Rehabilitation Exercises
The article presents University of Idaho-Physical Rehabilitation Movement Data (UI-PRMD), a publically available data set of movements related to common exercises performed by patients in physical rehabilitation programs. For the data collection, 10 healthy subjects performed 10 repetitions of different physical therapy movements with a Vicon optical tracker and a Microsoft Kinect sensor used for the motion capturing. The data are in a format that includes positions and angles of full-body joints. The objective of the data set is to provide a basis for mathematical modeling of therapy movements, as well as for establishing performance metrics for evaluation of patient consistency in executing the prescribed rehabilitation exercises