These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

315 related articles for article (PubMed ID: 28646155)

  • 1. Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model.
    Han Z; Wei B; Zheng Y; Yin Y; Li K; Li S
    Sci Rep; 2017 Jun; 7(1):4172. PubMed ID: 28646155
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Convolution Neural Network for Breast Cancer Detection and Classification Using Deep Learning.
    Abunasser BS; Al-Hiealy MRJ; Zaqout IS; Abu-Naser SS
    Asian Pac J Cancer Prev; 2023 Feb; 24(2):531-544. PubMed ID: 36853302
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Conventional Machine Learning and Deep Learning Approach for Multi-Classification of Breast Cancer Histopathology Images-a Comparative Insight.
    Sharma S; Mehra R
    J Digit Imaging; 2020 Jun; 33(3):632-654. PubMed ID: 31900812
    [TBL] [Abstract][Full Text] [Related]  

  • 4. MTRRE-Net: A deep learning model for detection of breast cancer from histopathological images.
    Chattopadhyay S; Dey A; Singh PK; Oliva D; Cuevas E; Sarkar R
    Comput Biol Med; 2022 Nov; 150():106155. PubMed ID: 36240595
    [TBL] [Abstract][Full Text] [Related]  

  • 5. MuDeRN: Multi-category classification of breast histopathological image using deep residual networks.
    Gandomkar Z; Brennan PC; Mello-Thoms C
    Artif Intell Med; 2018 Jun; 88():14-24. PubMed ID: 29705552
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Conventional Machine Learning versus Deep Learning for Magnification Dependent Histopathological Breast Cancer Image Classification: A Comparative Study with Visual Explanation.
    Boumaraf S; Liu X; Wan Y; Zheng Z; Ferkous C; Ma X; Li Z; Bardou D
    Diagnostics (Basel); 2021 Mar; 11(3):. PubMed ID: 33809611
    [TBL] [Abstract][Full Text] [Related]  

  • 7. CNN-based deep learning approach for classification of invasive ductal and metastasis types of breast carcinoma.
    Islam T; Hoque ME; Ullah M; Islam T; Nishu NA; Islam R
    Cancer Med; 2024 Aug; 13(16):e70069. PubMed ID: 39215495
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Classification of benign and malignant subtypes of breast cancer histopathology imaging using hybrid CNN-LSTM based transfer learning.
    Srikantamurthy MM; Rallabandi VPS; Dudekula DB; Natarajan S; Park J
    BMC Med Imaging; 2023 Jan; 23(1):19. PubMed ID: 36717788
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Assessment of deep learning algorithms to predict histopathological diagnosis of breast cancer: first Moroccan prospective study on a private dataset.
    El Agouri H; Azizi M; El Attar H; El Khannoussi M; Ibrahimi A; Kabbaj R; Kadiri H; BekarSabein S; EchCharif S; Mounjid C; El Khannoussi B
    BMC Res Notes; 2022 Feb; 15(1):66. PubMed ID: 35183227
    [TBL] [Abstract][Full Text] [Related]  

  • 10. LiverNet: efficient and robust deep learning model for automatic diagnosis of sub-types of liver hepatocellular carcinoma cancer from H&E stained liver histopathology images.
    Aatresh AA; Alabhya K; Lal S; Kini J; Saxena PUP
    Int J Comput Assist Radiol Surg; 2021 Sep; 16(9):1549-1563. PubMed ID: 34053009
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Breast cancer pathological image classification based on deep learning.
    Hou Y
    J Xray Sci Technol; 2020; 28(4):727-738. PubMed ID: 32390646
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Breast Cancer Classification in Automated Breast Ultrasound Using Multiview Convolutional Neural Network with Transfer Learning.
    Wang Y; Choi EJ; Choi Y; Zhang H; Jin GY; Ko SB
    Ultrasound Med Biol; 2020 May; 46(5):1119-1132. PubMed ID: 32059918
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Computer-aided diagnosis for breast cancer classification using deep neural networks and transfer learning.
    Aljuaid H; Alturki N; Alsubaie N; Cavallaro L; Liotta A
    Comput Methods Programs Biomed; 2022 Aug; 223():106951. PubMed ID: 35767911
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Label-Efficient Breast Cancer Histopathological Image Classification.
    Qi Q; Li Y; Wang J; Zheng H; Huang Y; Ding X; Rohde GK
    IEEE J Biomed Health Inform; 2019 Sep; 23(5):2108-2116. PubMed ID: 30530374
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module.
    Jiang Y; Chen L; Zhang H; Xiao X
    PLoS One; 2019; 14(3):e0214587. PubMed ID: 30925170
    [TBL] [Abstract][Full Text] [Related]  

  • 16. BCHisto-Net: Breast histopathological image classification by global and local feature aggregation.
    R R; Prasad K; Udupa CBK
    Artif Intell Med; 2021 Nov; 121():102191. PubMed ID: 34763806
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Classification of breast tumors by using a novel approach based on deep learning methods and feature selection.
    Kutluer N; Solmaz OA; Yamacli V; Eristi B; Eristi H
    Breast Cancer Res Treat; 2023 Jul; 200(2):183-192. PubMed ID: 37210703
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Breast cancer histopathological image classification using a hybrid deep neural network.
    Yan R; Ren F; Wang Z; Wang L; Zhang T; Liu Y; Rao X; Zheng C; Zhang F
    Methods; 2020 Feb; 173():52-60. PubMed ID: 31212016
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Patch-based system for Classification of Breast Histology images using deep learning.
    Roy K; Banik D; Bhattacharjee D; Nasipuri M
    Comput Med Imaging Graph; 2019 Jan; 71():90-103. PubMed ID: 30594745
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Dilated and soft attention-guided convolutional neural network for breast cancer histology images classification.
    Zhong Y; Piao Y; Zhang G
    Microsc Res Tech; 2022 Apr; 85(4):1248-1257. PubMed ID: 34859543
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 16.