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1
Shape, texture and statistical features for classification of benign and malignant vertebral compression fractures in magnetic resonance
Shape, texture and statistical features for classification of benign and malignant vertebral compression fractures in magnetic resonance
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Shape, texture and statistical features for classification of benign and malignant vertebral compression fractures in magnetic resonance

Lucas Frighetto-Pereira Rangaraj Mandayam Rangayyan; Guilherme Augusto Metzner; Paulo Mazzoncini de Azevedo-Marques; Marcello Henrique Nogueira-Barbosa

Computers in Biology and Medicine Kidlington v. 73, p. 147-156, 2016

Kidlington 2016

Localização: FMRP - Fac. Medicina de Ribeirão Preto    (pcd 2843776 Estantes Deslizantes )(Acessar)

2
A comprehensive review of deep learning in colon cancer
A comprehensive review of deep learning in colon cancer
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A comprehensive review of deep learning in colon cancer

Pacal, Ishak ; Karaboga, Dervis ; Basturk, Alper ; Akay, Bahriye ; Nalbantoglu, Ufuk

Computers in biology and medicine, 2020-11, Vol.126, p.104003-104003, Article 104003 [Periódico revisado por pares]

United States: Elsevier Ltd

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3
A state-of-the-art survey of artificial neural networks for Whole-slide Image analysis: From popular Convolutional Neural Networks to potential visual transformers
A state-of-the-art survey of artificial neural networks for Whole-slide Image analysis: From popular Convolutional Neural Networks to potential visual transformers
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A state-of-the-art survey of artificial neural networks for Whole-slide Image analysis: From popular Convolutional Neural Networks to potential visual transformers

Hu, Weiming ; Li, Xintong ; Li, Chen ; Li, Rui ; Jiang, Tao ; Sun, Hongzan ; Huang, Xinyu ; Grzegorzek, Marcin ; Li, Xiaoyan

Computers in biology and medicine, 2023-07, Vol.161, p.107034-107034, Article 107034 [Periódico revisado por pares]

United States: Elsevier Ltd

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4
PECI-Net: Bolus segmentation from video fluoroscopic swallowing study images using preprocessing ensemble and cascaded inference
PECI-Net: Bolus segmentation from video fluoroscopic swallowing study images using preprocessing ensemble and cascaded inference
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PECI-Net: Bolus segmentation from video fluoroscopic swallowing study images using preprocessing ensemble and cascaded inference

Park, Dougho ; Kim, Younghun ; Kang, Harim ; Lee, Junmyeoung ; Choi, Jinyoung ; Kim, Taeyeon ; Lee, Sangeok ; Son, Seokil ; Kim, Minsol ; Kim, Injung

Computers in biology and medicine, 2024-04, Vol.172, p.108241-108241, Article 108241 [Periódico revisado por pares]

United States: Elsevier Ltd

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5
A vision-based approach for fall detection using multiple cameras and convolutional neural networks: A case study using the UP-Fall detection dataset
A vision-based approach for fall detection using multiple cameras and convolutional neural networks: A case study using the UP-Fall detection dataset
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A vision-based approach for fall detection using multiple cameras and convolutional neural networks: A case study using the UP-Fall detection dataset

Espinosa, Ricardo ; Ponce, Hiram ; Gutiérrez, Sebastián ; Martínez-Villaseñor, Lourdes ; Brieva, Jorge ; Moya-Albor, Ernesto

Computers in biology and medicine, 2019-12, Vol.115, p.103520-103520, Article 103520 [Periódico revisado por pares]

United States: Elsevier Ltd

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6
Painless and accurate medical image analysis using deep reinforcement learning with task-oriented homogenized automatic pre-processing
Painless and accurate medical image analysis using deep reinforcement learning with task-oriented homogenized automatic pre-processing
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Painless and accurate medical image analysis using deep reinforcement learning with task-oriented homogenized automatic pre-processing

Yuan, Di ; Liu, Yunxin ; Xu, Zhenghua ; Zhan, Yuefu ; Chen, Junyang ; Lukasiewicz, Thomas

Computers in biology and medicine, 2023-02, Vol.153, p.106487-106487, Article 106487 [Periódico revisado por pares]

United States: Elsevier Ltd

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7
TMS-Net: A segmentation network coupled with a run-time quality control method for robust cardiac image segmentation
TMS-Net: A segmentation network coupled with a run-time quality control method for robust cardiac image segmentation
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TMS-Net: A segmentation network coupled with a run-time quality control method for robust cardiac image segmentation

Uslu, Fatmatülzehra ; Bharath, Anil A.

Computers in biology and medicine, 2023-01, Vol.152, p.106422-106422, Article 106422 [Periódico revisado por pares]

United States: Elsevier Ltd

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8
Recent progress in transformer-based medical image analysis
Recent progress in transformer-based medical image analysis
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Recent progress in transformer-based medical image analysis

Liu, Zhaoshan ; Lv, Qiujie ; Yang, Ziduo ; Li, Yifan ; Lee, Chau Hung ; Shen, Lei

Computers in biology and medicine, 2023-09, Vol.164, p.107268-107268, Article 107268 [Periódico revisado por pares]

United States: Elsevier Ltd

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9
Deep learning based joint segmentation and characterization of multi-class retinal fluid lesions on OCT scans for clinical use in anti-VEGF therapy
Deep learning based joint segmentation and characterization of multi-class retinal fluid lesions on OCT scans for clinical use in anti-VEGF therapy
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Deep learning based joint segmentation and characterization of multi-class retinal fluid lesions on OCT scans for clinical use in anti-VEGF therapy

Hassan, Bilal ; Qin, Shiyin ; Ahmed, Ramsha ; Hassan, Taimur ; Taguri, Abdel Hakeem ; Hashmi, Shahrukh ; Werghi, Naoufel

Computers in biology and medicine, 2021-09, Vol.136, p.104727-104727, Article 104727 [Periódico revisado por pares]

Oxford: Elsevier Ltd

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10
SegR-Net: A deep learning framework with multi-scale feature fusion for robust retinal vessel segmentation
SegR-Net: A deep learning framework with multi-scale feature fusion for robust retinal vessel segmentation
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SegR-Net: A deep learning framework with multi-scale feature fusion for robust retinal vessel segmentation

Ryu, Jihyoung ; Rehman, Mobeen Ur ; Nizami, Imran Fareed ; Chong, Kil To

Computers in biology and medicine, 2023-09, Vol.163, p.107132-107132, Article 107132 [Periódico revisado por pares]

United States: Elsevier Ltd

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