Literature DB >> 24579167

Mitosis detection in breast cancer histology images with deep neural networks.

Dan C Cireşan1, Alessandro Giusti2, Luca M Gambardella2, Jürgen Schmidhuber2.   

Abstract

We use deep max-pooling convolutional neural networks to detect mitosis in breast histology images. The networks are trained to classify each pixel in the images, using as context a patch centered on the pixel. Simple postprocessing is then applied to the network output. Our approach won the ICPR 2012 mitosis detection competition, outperforming other contestants by a significant margin.

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Year:  2013        PMID: 24579167     DOI: 10.1007/978-3-642-40763-5_51

Source DB:  PubMed          Journal:  Med Image Comput Comput Assist Interv


  196 in total

1.  Diagnosis of breast cancer in light microscopic and mammographic images textures using relative entropy via kernel estimation.

Authors:  Sevcan Aytac Korkmaz; Mehmet Fatih Korkmaz; Mustafa Poyraz
Journal:  Med Biol Eng Comput       Date:  2015-09-07       Impact factor: 2.602

2.  A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images.

Authors:  Andrew Janowczyk; Scott Doyle; Hannah Gilmore; Anant Madabhushi
Journal:  Comput Methods Biomech Biomed Eng Imaging Vis       Date:  2016-04-28

3.  Automated detection of coarctation of aorta in neonates from two-dimensional echocardiograms.

Authors:  Franklin Pereira; Alejandra Bueno; Andrea Rodriguez; Douglas Perrin; Gerald Marx; Michael Cardinale; Ivan Salgo; Pedro Del Nido
Journal:  J Med Imaging (Bellingham)       Date:  2017-01-24

Review 4.  Segmentation of joint and musculoskeletal tissue in the study of arthritis.

Authors:  Valentina Pedoia; Sharmila Majumdar; Thomas M Link
Journal:  MAGMA       Date:  2016-02-25       Impact factor: 2.310

5.  Analysis of tumor nuclear features using artificial intelligence to predict response to neoadjuvant chemotherapy in high-risk breast cancer patients.

Authors:  David W Dodington; Andrew Lagree; Sami Tabbarah; Majid Mohebpour; Ali Sadeghi-Naini; William T Tran; Fang-I Lu
Journal:  Breast Cancer Res Treat       Date:  2021-01-23       Impact factor: 4.872

6.  A deep learning based strategy for identifying and associating mitotic activity with gene expression derived risk categories in estrogen receptor positive breast cancers.

Authors:  David Romo-Bucheli; Andrew Janowczyk; Hannah Gilmore; Eduardo Romero; Anant Madabhushi
Journal:  Cytometry A       Date:  2017-02-13       Impact factor: 4.355

7.  Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks.

Authors:  Mingchen Gao; Ulas Bagci; Le Lu; Aaron Wu; Mario Buty; Hoo-Chang Shin; Holger Roth; Georgios Z Papadakis; Adrien Depeursinge; Ronald M Summers; Ziyue Xu; Daniel J Mollura
Journal:  Comput Methods Biomech Biomed Eng Imaging Vis       Date:  2016-06-06

8.  Center-Focusing Multi-task CNN with Injected Features for Classification of Glioma Nuclear Images.

Authors:  Veda Murthy; Le Hou; Dimitris Samaras; Tahsin M Kurc; Joel H Saltz
Journal:  IEEE Winter Conf Appl Comput Vis       Date:  2017-05-15

Review 9.  Breast cancer cell nuclei classification in histopathology images using deep neural networks.

Authors:  Yangqin Feng; Lei Zhang; Zhang Yi
Journal:  Int J Comput Assist Radiol Surg       Date:  2017-08-31       Impact factor: 2.924

10.  AUTOMATIC MUSCLE PERIMYSIUM ANNOTATION USING DEEP CONVOLUTIONAL NEURAL NETWORK.

Authors:  Manish Sapkota; Fuyong Xing; Hai Su; Lin Yang
Journal:  Proc IEEE Int Symp Biomed Imaging       Date:  2015-07-23
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