Restricted Boltzmann machines based oversampling and semi-supervised learning for false positive reduction in breast CAD

Cao, Peng; Liu, Xiaoli; Bao, Hang; Yang, Jinzhu; Zhao, Dazhe

doi:10.3233/bme-151453

Cited by 9 publications

(3 citation statements)

References 13 publications

(12 reference statements)

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“…In medical image analysis, unsupervised learning excels in different classification tasks like benign and malignant tumor detection [37][38][39], domain adaptation in cardiac arrhythmia classification [40,41], brain disorder classification [42][43][44], and mass detection in beast cancer [45].…”

Section: Unsupervised Learningmentioning

confidence: 99%

Machine Learning Empowering Personalized Medicine: A Comprehensive Review of Medical Image Analysis Methods

Galić,

Habijan,

Leventić

et al. 2023

Electronics

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Artificial intelligence (AI) advancements, especially deep learning, have significantly improved medical image processing and analysis in various tasks such as disease detection, classification, and anatomical structure segmentation. This work overviews fundamental concepts, state-of-the-art models, and publicly available datasets in the field of medical imaging. First, we introduce the types of learning problems commonly employed in medical image processing and then proceed to present an overview of commonly used deep learning methods, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), with a focus on the image analysis task they are solving, including image classification, object detection/localization, segmentation, generation, and registration. Further, we highlight studies conducted in various application areas, encompassing neurology, brain imaging, retinal analysis, pulmonary imaging, digital pathology, breast imaging, cardiac imaging, bone analysis, abdominal imaging, and musculoskeletal imaging. The strengths and limitations of each method are carefully examined, and the paper identifies pertinent challenges that still require attention, such as the limited availability of annotated data, variability in medical images, and the interpretability issues. Finally, we discuss future research directions with a particular focus on developing explainable deep learning methods and integrating multi-modal data.

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Section: Unsupervised Learningmentioning

confidence: 99%

Machine Learning Empowering Personalized Medicine: A Comprehensive Review of Medical Image Analysis Methods

Galić,

Habijan,

Leventić

et al. 2023

Electronics

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“…The constrained connectivity between the layers makes it feasible to construct deeper models for efficient learning. RBMs have been extensively used in various parts of medical image analysis, such as image segmentation [83], feature learning [84], disease classification [85], mass detection in breast cancer [86], and brain lesion segmentation [84].…”

Section: Restricted Boltzmann Machines (Rbm)mentioning

confidence: 99%

A Review on Progress in Semantic Image Segmentation and Its Application to Medical Images

Kar¹,

Nath²,

Neog³

2021

SN COMPUT. SCI.

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Semantic image segmentation is a popular image segmentation technique where each pixel in an image is labeled with an object class. This technique has become a vital part of image analysis nowadays as it facilitates the description, categorization, and visualization of the regions of interest in an image. The recent developments in computer vision algorithms and the increasing availability of large datasets have made semantic image segmentation very popular in the field of computer vision. Motivated by the human visual system which can identify objects in a complex scene very efficiently, researchers are interested in building a model that can semantically segment an image into meaningful object classes. This paper reviews deep learning-based semantic segmentation techniques that use deep neural network architectures for image segmentation of biomedical images. We have provided a discussion on the fundamental concepts related to deep learning methods used in semantic segmentation for the benefit of readers. The standard datasets and existing deep network architectures used in both medical and non-medical fields are discussed with their significance. Finally, this paper concludes by discussing the challenges and future research directions in the field of deep learning-based semantic segmentation for applications in the medical field.

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“…[74] Lesões de esclerose múltipla 3DMRI Usa de imagens de MR multicanal 3D de lesão de esclerose múltipla(EM) para segmentação MS. [65] AD/MCI/HC classificação MRI,PET DBMs em imagens multimodais de resonância MRI e PET digitalizadas para classificação de doenças. [16] Detecção de massa no câncer de mama Mamografia Método baseado em RBM para oversampling de câncer e aprendizado semi-supervisionado para resolver a classificação de dados desbalanceados com algumas amostras rotuladas. [36] Separação da origem cega de fMRI fMRI RBM usado para detecção de fonte latente induzida por interação funcional interna e de separação.…”

Section: Ref Tarefaunclassified

Arquitetura brasileira da redemocratização ao Plano Real

Facion¹

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Gostaria de agradecer a oportunidade de estar fazendo mestrado em uma das maiores universidades do mundo. De ter oportunidade também de ser orientada pela Prof. Dra. Esther Luna Colombini, que além de me auxiliar durante todo o mestrado, não desistiu de mim nos momentos difíceis por quais passei.Agradeço ao meu marido João Eduardo Brandes Luiz, que me deu forças a todo momento, pelas noites que passou em claro comigo, as revisões que realizava nos meus textos e por acreditar em mim quando nem eu acreditava mais.Quero lembrar também de alguns professores que foram os responsáveis por me inspirarem e terem mudado minha vida de alguma forma. Penso que ser professor é mais do que dar aulas e fazer pesquisas, é poder fazer a diferença na vida de pessoas. Esse é o meu propósito de vida, e pelo qual estou lutando muito, pois acredito que é a essência da minha felicidade. Estes são os responsáveis por eu querer ser professora um dia, então meus agradecimentos vão a:

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Restricted Boltzmann machines based oversampling and semi-supervised learning for false positive reduction in breast CAD

Cited by 9 publications

References 13 publications

Machine Learning Empowering Personalized Medicine: A Comprehensive Review of Medical Image Analysis Methods

Machine Learning Empowering Personalized Medicine: A Comprehensive Review of Medical Image Analysis Methods

A Review on Progress in Semantic Image Segmentation and Its Application to Medical Images

Arquitetura brasileira da redemocratização ao Plano Real

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