Computer-aided detection of abnormality in mammography using deep object detectors

[1]  S. Duffy,et al.  Comparison of single reading with double reading of mammograms, and change in effectiveness with experience. , 1995, The British journal of radiology.

[2]  Bolei Zhou,et al.  Learning Deep Features for Discriminative Localization , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Lazaros T. Tsochatzidis,et al.  Deep Learning for Breast Cancer Diagnosis from Mammograms—A Comparative Study , 2019, J. Imaging.

[4]  Reyer Zwiggelaar,et al.  Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks , 2018, IEEE Journal of Biomedical and Health Informatics.

[5]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Yoshua Bengio,et al.  How transferable are features in deep neural networks? , 2014, NIPS.

[7]  Sergio Guadarrama,et al.  Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Bo Wang,et al.  Weakly supervised mitosis detection in breast histopathology images using concentric loss , 2019, Medical Image Anal..

[9]  Hiba Chougrad,et al.  Multi-label transfer learning for the early diagnosis of breast cancer , 2020, Neurocomputing.

[10]  Seokmin Han,et al.  A deep learning framework for supporting the classification of breast lesions in ultrasound images , 2017, Physics in medicine and biology.

[11]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Xiaochun Cao,et al.  Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation , 2018, IEEE Transactions on Medical Imaging.

[13]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[14]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[15]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  B. Andersen,et al.  Breast Cancer: Prevention and Control , 1992 .

[17]  Q. M. Jonathan Wu,et al.  Automatic Kidney Lesion Detection for CT Images Using Morphological Cascade Convolutional Neural Networks , 2019, IEEE Access.

[18]  Ali Farhadi,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.

[19]  Pengcheng Xi,et al.  Cardiac Murmur Classification in Phonocardiograms Using Deep Recurrent-Convolutional Neural Networks , 2019 .

[20]  Xavier Lladó,et al.  Automatic mass detection in mammograms using deep convolutional neural networks , 2019, Journal of medical imaging.

[21]  Mokhtar Sellami,et al.  CAD system for classification of mammographic abnormalities using transductive semi supervised learning algorithm and heterogeneous features , 2015, 2015 12th International Symposium on Programming and Systems (ISPS).

[22]  Daniel L Rubin,et al.  A curated mammography data set for use in computer-aided detection and diagnosis research , 2017, Scientific Data.

[23]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Mark Sandler,et al.  MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[25]  A. Ramli,et al.  Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. , 2013, Clinical imaging.

[26]  Yongyi Yang,et al.  Computer-Aided Detection and Diagnosis of Breast Cancer With Mammography: Recent Advances , 2009, IEEE Transactions on Information Technology in Biomedicine.

[27]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[28]  Stephen Lin,et al.  DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field , 2016, MICCAI.

[29]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[30]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[31]  M. A. Al-masni,et al.  Detection and classification of the breast abnormalities in digital mammograms via regional Convolutional Neural Network , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[32]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[33]  Rafik Goubran,et al.  Abnormality Detection in Mammography using Deep Convolutional Neural Networks , 2018, 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA).

[34]  Houjin Chen,et al.  A Survey of Computer-aided Detection of Breast Cancer with Mammography , 2016 .

[35]  Xin Jin,et al.  K-Medoids Clustering , 2010, Encyclopedia of Machine Learning.

[36]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[37]  R. Castellino,et al.  Computer aided detection (CAD): an overview , 2005, Cancer imaging : the official publication of the International Cancer Imaging Society.