Weakly supervised 3D deep learning for breast cancer classification and localization of the lesions in MR images

The usefulness of 3D deep learning‐based classification of breast cancer and malignancy localization from MRI has been reported. This work can potentially be very useful in the clinical domain and aid radiologists in breast cancer diagnosis.

[1]  Maximilian Reiser,et al.  Prospective multicenter cohort study to refine management recommendations for women at elevated familial risk of breast cancer: the EVA trial. , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[2]  Michael Götz,et al.  Prediction of malignancy by a radiomic signature from contrast agent‐free diffusion MRI in suspicious breast lesions found on screening mammography. , 2017, Journal of magnetic resonance imaging : JMRI.

[3]  Vladlen Koltun,et al.  Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials , 2011, NIPS.

[4]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[5]  I. Masroor,et al.  To asses inter- and intra-observer variability for breast density and BIRADS assessment categories in mammographic reporting. , 2016, JPMA. The Journal of the Pakistan Medical Association.

[6]  Ronald M. Summers,et al.  ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases , 2019, Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics.

[7]  A. Agresti,et al.  Approximate is Better than “Exact” for Interval Estimation of Binomial Proportions , 1998 .

[8]  A. Jemal,et al.  Breast cancer statistics, 2017, racial disparity in mortality by state , 2017, CA: a cancer journal for clinicians.

[9]  Haydee Ojeda-Fournier,et al.  A Pictorial Review of Changes in the BI-RADS Fifth Edition. , 2016, Radiographics : a review publication of the Radiological Society of North America, Inc.

[10]  A. Eisen,et al.  Optimal systemic therapy for early breast cancer in women: a clinical practice guideline. , 2014, Current oncology.

[11]  Guy Amit,et al.  Classification of breast MRI lesions using small-size training sets: comparison of deep learning approaches , 2017, Medical Imaging.

[12]  Alejandro F. Frangi,et al.  Muliscale Vessel Enhancement Filtering , 1998, MICCAI.

[13]  Youbao Tang,et al.  Accurate Weakly-Supervised Deep Lesion Segmentation using Large-Scale Clinical Annotations: Slice-Propagated 3D Mask Generation from 2D RECIST , 2018, MICCAI.

[14]  Gustavo Carneiro,et al.  Deep Reinforcement Learning for Active Breast Lesion Detection from DCE-MRI , 2017, MICCAI.

[15]  Hao Chen,et al.  Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge , 2016, Medical Image Anal..

[16]  B. Preim,et al.  Computer-aided diagnosis in breast DCE-MRI--quantification of the heterogeneity of breast lesions. , 2012, European journal of radiology.

[17]  Hao Chen,et al.  3D deeply supervised network for automated segmentation of volumetric medical images , 2017, Medical Image Anal..

[18]  Berkman Sahiner,et al.  Detection and diagnosis of colitis on computed tomography using deep convolutional neural networks , 2017, Medical physics.

[19]  Bolei Zhou,et al.  Learning Deep Features for Scene Recognition using Places Database , 2014, NIPS.

[20]  C. Boetes,et al.  Breast MRI: guidelines from the European Society of Breast Imaging , 2008, European Radiology.

[21]  Yuan Gao,et al.  Detection and Characterization of the Fetal Heartbeat in Free-hand Ultrasound Sweeps with Weakly-supervised Two-streams Convolutional Networks , 2017, MICCAI.

[22]  L. R. Dice.  Measures of the Amount of Ecologic Association Between Species , 1945 .

[23]  Konstantinos Kamnitsas,et al.  Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation , 2016, Medical Image Anal..

[24]  Ronald M. Summers,et al.  ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases , 2019, Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics.

[25]  Q. Mcnemar.  Note on the sampling error of the difference between correlated proportions or percentages , 1947, Psychometrika.

[26]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[27]  L. Esserman,et al.  Locally advanced breast cancer: MR imaging for prediction of response to neoadjuvant chemotherapy--results from ACRIN 6657/I-SPY TRIAL. , 2012, Radiology.

[28]  Karel G M Moons,et al.  Meta-analysis of MR imaging in the diagnosis of breast lesions. , 2008, Radiology.

[29]  Gustavo Carneiro,et al.  The Automated Learning of Deep Features for Breast Mass Classification from Mammograms , 2016, MICCAI.

[30]  Ellen Warner,et al.  Effectiveness of screening with annual magnetic resonance imaging and mammography: results of the initial screen from the ontario high risk breast screening program. , 2014, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[31]  N. Obuchowski.  Receiver operating characteristic curves and their use in radiology. , 2003, Radiology.