Recent Advances in Medical Image Processing

2.

Simonyan

K

,

Zisserman

A

.

Very deep convolution networks for large scale image recognition

. In:

International conference on learning representations

;

2015

.

3.

He

K

,

Zhang

X

,

Ren

S

,

Sun

J

.

Deep residual learning for image recognition

.

The IEEE conference on computer vision and pattern recognition (CVPR)

;

2016

.

4.

McCarthy

J

,

Minsky

ML

,

Rochester

N

,

Shannon

CE

.

A proposal for the Dartmouth summer research project on artificial intelligence, August 31, 1955

.

AI Mag

.

2006

;

27

(

4

):

12

.

5.

Deng

J

,

Dong

W

,

Socher

R

,

Li

LJ

,

Li

K

,

Fei-Fei

L

.

ImageNet: a large scale hierarchical image database

.

2009 IEEE conference on computer vision and pattern recognition

.

IEEE

;

2009

. p.

248

–

55

.

https://doi.org/10.1162/neco.2006.18.7.1527

6.

Hinton

GE

,

Osindero

S

,

Teh

YW

.

A fast learning algorithm for deep belief nets

.

Neural Comput

.

2006

;

18

(

7

):

1527

–

54

.

.

https://doi.org/10.4249/scholarpedia.5947

7.

Hinton

G

.

Deep belief networks

.

Scholarpedia

.

2009

;

4

(

5

):

5947

.

.

https://doi.org/10.1038/nature14539

8.

LeCun

Y

,

Bengio

Y

,

Hinton

G

.

Deep learning

.

Nature

.

2015

;

521

(

7553

):

436

–

44

.

.

https://doi.org/10.1109/78.650093

9.

Schuster

M

,

Paliwal

KK

.

Bidirectional recurrent neural networks

.

IEEE Trans Signal Process

.

1997

;

45

(

11

):

2673

–

81

.

.

10.

Hou

L

,

Samaras

D

,

Kurc

TM

,

Gao

Y

,

Davis

JE

,

Saltz

JH

.

Patch-based convolution neural network for whole slide tissue image classification

. In:

Proceedings of the IEEE conference on computer vision and pattern recognition

;

2016

. p.

2424

–

33

.

11.

Rajpurkar

P

,

Irvin

J

,

Zhu

K

,

Yang

B

,

Mehta

H

,

Duan

T

,

CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning

. arXiv preprint arXiv:171105225.

2017

.

https://doi.org/10.1001/jama.2016.17216

12.

Gulshan

V

,

Peng

L

,

Coram

M

,

Stumpe

MC

,

Wu

D

,

Narayanaswamy

A

,

Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs

.

JAMA

.

2016

;

316

(

22

):

2402

–

10

.

.

13.

Girshick

R

,

Donahue

J

,

Darrell

T

,

Malik

J

.

Rich feature hierarchies for accurate object detection and semantic segmentation

. In:

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

;

2014

. p.

580

–

87

.

14.

He

K

,

Gkioxari

G

,

Dollár

P

,

Girshick

R

.

Mask r-cnn

. In:

Proceedings of the IEEE International Conference on Computer Vision

;

2017

. p.

2961

–

69

.

15.

Collobert

R

,

Weston

J

,

Bottou

L

,

Karlen

M

,

Kavukcuoglu

K

,

Kuksa

P

.

Natural language processing (almost) from scratch

.

J Mach Learn Res

.

2011

;

12

(

Aug

):

2493

–

537

.

16.

Manning

CD

,

Surdeanu

M

,

Bauer

J

,

Finkel

JR

,

Bethard

S

,

McClosky

D

.

The Stanford CoreNLP natural language processing toolkit

. In:

Proceed-ings of 52nd annual meeting of the association for computational linguistics: system demonstrations

;

2014

. p.

55

–

60

.

17.

Fukushima

K

.

Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaﬀected by shift in position

.

Biol Cybern

.

1980

;

36

(

4

):

193

–

202

.

https://doi.org/10.1016/0893-6080(89)90041-5

18.

Fukushima

K

.

Analysis of the process of visual pattern recognition by the neocognitron

.

Neural Networks

.

1989

;

2

(

6

):

413

–

20

.

.

https://doi.org/10.1162/neco.1989.1.4.541

19.

LeCun

Y

,

Boser

B

,

Denker

JS

,

Henderson

D

,

Howard

RE

,

Hubbard

W

,

Backpropagation applied to handwritten zip code recognition

.

Nat Commun

.

1989

;

1

(

4

):

541

–

51

.

.

https://doi.org/10.1109/5.726791

20.

LeCun

Y

,

Bottou

L

,

Bengio

Y

,

Haﬀner

P

.

Gradient-based learning applied to document recognition

.

Proc IEEE

.

1998

;

86

(

11

):

2278

–

324

.

.

21.

Girshick

R

.

Fast r-cnn

. In:

Proceedings of the IEEE International Conference on Computer Vision

;

2015

. p.

1440

–

48

.

22.

Redmon

J

,

Divvala

S

,

Girshick

R

,

Farhadi

A

.

You only look once: unified, real-time object detection

. In:

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

;

2016

. p.

779

–

88

.

23.

Liu

W

,

Anguelov

D

,

Erhan

D

,

Szegedy

C

,

Reed

S

,

Fu

CY

,

Ssd: single shot multibox detector

.

European Conference on Computer Vision

.

Springer

;

2016

. p.

21

–

37

.

24.

Chen

LC

,

Papandreou

G

,

Kokkinos

I

,

Murphy

K

,

Yuille

AL

.

DeepLab: semantic image segmentation with deep convolution nets, atrous convolution, and fully connected CRFS

.

IEEE Trans Pattern Anal Mach Intel

.

2017

;

40

(

4

):

834

–

48

.

25.

Badrinarayanan

V

,

Kendall

A

,

Cipolla

R

.

SegNet: a deep convolution encoder decoder architecture for image segmentation

.

IEEE Trans Pattern Anal Mach Intel

.

2017

;

39

(

12

):

2481

–

95

.

26.

Cordts

M

,

Omran

M

,

Ramos

S

,

Rehfeld

T

,

Enzweiler

M

,

Benenson

R

,

The cityscapes dataset for semantic urban scene understanding

. In:

Proceedings of the IEEE conference on computer vision and pattern recognition

;

2016

, p.

3213

–

23

.

27.

Lin

G

,

Milan

A

,

Shen

C

,

Reid

I

.

RefineNet: multi-path refinement networks for high-resolution semantic segmentation

. In:

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

;

2017

. p.

1925

–

34

.

28.

Simonyan

K

,

Vedaldi

A

,

Zisserman

A

.

Deep inside convolution networks: visualising image classification models and saliency maps

. arXiv preprint arXiv:13126034. 2013.

29.

Xie

Y

,

Kong

X

,

Xing

F

,

Liu

F

,

Su

H

,

Yang

L

.

Deep voting: a robust approach toward nucleus localization in microscopy images

.

International conference on medical image computing and computer-ssisted Intervention

.

Springer

;

2015

. p.

374

–

82

.

https://doi.org/10.4103/jpi.jpi_43_18

30.

Sanyal

P

,

Mukherjee

T

,

Barui

S

,

Das

A

,

Gangopadhyay

P

.

Artificial intelligence in cytopathology: a neural network to identify papillary carcinoma on thyroid fine-needle aspiration cytology smears

.

J Pathol Inform

.

2018

;

9

:

43

.

.

https://doi.org/10.1002/cncy.22176

31.

Sanghvi

AB

,

Allen

EZ

,

Callenberg

KM

,

Pantanowitz

L

.

Performance of an artificial intelligence algorithm for reporting urine cytopathology

.

Cancer Cytopathol

.

2019 Oct

;

127

(

10

):

658

–

66

.

.

https://doi.org/10.1038/s41598-017-07599-6

32.

Sadanandan

SK

,

Ranefall

P

,

Le Guyader

S

,

Wählby

C

.

Automated training of deep convolutional neural networks for cell segmentation

.

Sci Rep

.

2017

;

7

(

1

):

7860

–

7

.

.

33.

Liang

Z

,

Powell

A

,

Ersoy

I

,

Poostchi

M

,

Silamut

K

,

Palaniappan

K

,

CNN-based image analysis for malaria diagnosis

. In:

2016 IEEE international conference on bioinformatics and biomedicine (BIBM)

.

IEEE

;

2016 Dec 15

. p.

493

–

6

.

34.

Li

X

,

Li

W

,

Xu

X

,

Hu

W

.

Cell classification using convolutional neural networks in medical hyperspectral imagery

. In:

2017 2nd international conference on image, vision and computing (ICIVC)

.

IEEE

;

2017 Jun 2

. p.

501

–

4

.

35.

Ciresan

DC

,

Giusti

A

,

Gambardella

LM

,

Schmidhuber

J

.

Mitosis detection in breast cancer histology images with deep neural networks

.

International conference on medical image computing and computer-assisted intervention

.

Springer

;

2013

. p.

411

–

8

.

https://doi.org/10.1371/journal.pone.0177544

36.

Araújo

T

,

Aresta

G

,

Castro

E

,

Rouco

J

,

Aguiar

P

,

Eloy

C

,

Classification of breast cancer histology images using convolutional neural networks

.

PLoS One

.

2017

;

12

(

6

):

e0177544

.

.

https://doi.org/10.1109/access.2018.2831280

37.

Bardou

D

,

Zhang

K

,

Ahmad

SM

.

Classification of breast cancer based on histology images using convolutional neural networks

.

IEEE Access

.

2018

;

6

:

24680

–

93

.

.

38.

Li

Y

,

Ping

W

.

Cancer metastasis detection with neural conditional random field

. arXiv preprint arXiv: 180607064.

2018

.

39.

Szegedy

C

,

Vanhoucke

V

,

Ioﬀe

S

,

Shlens

J

,

Wojna

Z

.

Rethinking the inception architecture for computer vision

. In:

Proceedings of the IEEE conference on computer vision and pattern recognition

;

2016

. p.

2818

–

26

.

https://doi.org/10.1109/access.2019.2903171

40.

Zeng

X

,

Chen

H

,

Luo

Y

,

Ye

W

.

Automated diabetic retinopathy detection based on binocular Siamese-like convolutional neural network

.

IEEE Access

.

2019

;

7

:

30744

–

53

.

.

41.

Wang

X

,

Peng

Y

,

Lu

L

,

Lu

Z

,

Bagheri

M

,

Summers

RM

.

ChestX-ray8: hospital scale chest X-ray database and benchmarks on weakly supervised classification and localization of common thorax diseases

. In:

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

;

2017

. p.

2097

–

106

.

42.

Zhou

B

,

Khosla

A

,

Lapedriza

A

,

Oliva

A

,

Torralba

A

.

Learning deep features for discriminative localization

. In:

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

;

2016

. p.

2921

–

29

.

43.

Li

L

,

Qin

L

,

Xu

Z

,

Yin

Y

,

Wang

X

,

Kong

B

,

Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT

.

Radiology

.

2020

:

200905

.

44.

Wang

L

,

Wong

A

.

COVID-Net: a tailored deep convolution neural network design for detection of COVID-19 cases from chest radiography images

. arXiv preprint arXiv:200309871.

2020

.

45.

Apostolopoulos

ID

,

Mpesiana

TA

.

Covid-19: automatic detection from X-ray images utilizing transfer learning with convolution neural networks

.

Phys Eng Sci Med

.

2020

;

43

(

2

):

635

–

40

.

46.

Ciresan

D

,

Giusti

A

,

Gambardella

LM

,

Schmidhuber

J

.

Deep neural networks segment neuronal membranes in electron microscopy images

.

Adv Neural Inf Process Syst

.

2012

;

843

–

51

.

47.

Ronneberger

O

,

Fischer

P

,

Brox

T

.

U-Net: convolution networks for biomedical image segmentation

. In:

International Conference on Medical Image Computing and Computer-Assisted Intervention

.

Springer

;

2015

. p.

234

–

41

.

48.

Milletari

F

,

Navab

N

,

Ahmadi

SA

.

V-Net: fully convolution neural networks for volumetric medical image segmentation

. In:

2016 Fourth International Conference on 3D Vision (3DV)

.

IEEE

;

2016

. p.

565

–

71

.

https://doi.org/10.1016/j.media.2016.10.004

49.

Kamnitsas

K

,

Ledig

C

,

Newcombe

VF

,

Simpson

JP

,

Kane

AD

,

Menon

DK

,

Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation

.

Med Image Anal

.

2017

;

36

:

61

–

78

.

.

50.

Zoph

B

,

Le

QV

.

Neural architecture search with reinforcement learning

. arXiv preprint arXiv:161101578.

2016

.

51.

Liu

C

,

Chen

LC

,

Schroﬀ

F

,

Adam

H

,

Hua

W

,

Yuille

AL

,

Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation

. In:

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

;

2019

. p.

82

–

92

.

52.

Hecht-Nielsen

R

.

Theory of the backpropagation neural network

.

Neural networks for perception

.

Elsevier

;

1992

. p.

65

–

93

.

53.

Rumelhart

DE

,

Hinton

GE

,

Williams

RJ

.

Learning internal representations by error propagation

.

California Univ San Diego La Jolla Inst for Cognitive Science

;

1985

.

54.

Rumelhart

DE

,

Durbin

R

,

Golden

R

,

Chauvin

Y

.

Backpropagation: the basic theory

.

Backpropagation: theory, architectures and applications

.

1995

. p.

1

–

34

.

https://doi.org/10.1023/b:visi.0000029664.99615.94

55.

Lowe

DG

.

Distinctive image features from scale-invariant keypoints

.

Int J Comput Vis

.

2004 Nov

;

60

(

2

):

91

–

110

.

.

56.

Lazebnik

S

,

Schmid

C

,

Ponce

J

.

Beyond bags of features: spatial pyramid matching for recognizing natural scene categories

. In:

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06)

;

2006

.

Vol. 2

; p.

2169

–

78

.

57.

Le

H

,

Borji

A

.

What are the receptive, eﬀective receptive, and projective fields of neurons in convolution neural networks?

arXiv preprint arXiv:170507049.

2017

.

https://doi.org/10.3844/ajassp.2009.1502.1508

58.

Ghrare

SE

,

Mohd. Ali

MA

,

Jumari

K

,

Ismail

M

.

An efficient low complexity lossless coding algorithm for medical images

.

Am J App Sci

.

2009

;

6

(

8

):

1502

–

8

.

.

59.

Kil

SK

,

Lee

JS

,

Shen

DF

,

Ryu

J

,

Lee

EH

,

Min

HK

,

Lossless medical image compression using redundancy analysis

.

2006

.

60.

Xie

C

,

Wu

Y

,

Maaten

Lvd

,

Yuille

AL

,

He

K

.

Feature denoising for improving adversarial robustness

. In:

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

;

2019

. p.

501

–

9

.

61.

Xie

S

,

Girshick

R

,

Dollár

P

,

Tu

Z

,

He

K

.

Aggregated residual transformations for deep neural networks

. In:

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

;

2017

. p.

1492

–

1500

.

62.

Szegedy

C

,

Liu

W

,

Jia

Y

,

Sermanet

P

,

Reed

S

,

Anguelov

D

,

Going deeper with convolutions

. In:

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

;

2015

. p.

1

–

9

.

63.

Szegedy

C

,

Ioﬀe

S

,

Vanhoucke

V

,

Alemi

AA

.

Inception-v4, Inception-ResNet and the impact of residual connections on learning

. In:

Thirty-First AAAI Conference on Artificial Intelligence

;

2017

.

64.

Ma

N

,

Zhang

X

,

Zheng

HT

,

Sun

J

.

ShuﬄeNet v2: Practical guidelines for eﬃcient cnn architecture design

. In:

Proceedings of the European Conference on Computer Vision (ECCV)

;

2018

. p.

116

–

31

.

65.

Freeman

I

,

Roese Koerner

L

,

Kummert

A

.

EﬀNet: an eﬃcient structure for convolution neural networks

. In:

2018 25th IEEE International Conference on Image Processing (ICIP)

.

IEEE

;

2018

. p.

6

–

10

.

66.

Zoph

B

,

Vasudevan

V

,

Shlens

J

,

Le

QV

.

Learning transferable architectures for scalable image recognition

. In:

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

;

2018

. p.

8697

–

710

.

67.

Zhong

Z

,

Yan

J

,

Liu

CL

.

Practical network blocks design with q-learning

. arXiv preprint arXiv:170805552.

2017

. p.

6

.

68.

Liu

H

,

Simonyan

K

,

Yang

Y

.

Darts: diﬀerentiable architecture search

. arXiv preprint arXiv:180609055.

2018

.

69.

Real

E

,

Moore

S

,

Selle

A

,

Saxena

S

,

Suematsu

YL

,

Tan

J

,

Large scale evolution of image classifiers

. In:

Proceedings of the 34th International Conference on Machine Learning-Volume 70

.

JMLR.org

;

2017

. p.

2902

–

11

.

70.

Xie

L

,

Yuille

A

.

Genetic cnn

. In:

Proceedings of the IEEE International Conference on Computer Vision

;

2017

. p.

1379

–

88

.

71.

Haeﬀele

BD

,

Vidal

R

.

Global optimality in neural network training

. In:

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

;

2017

. p.

7331

–

39

.

72.

Shwartz-Ziv

R

,

Tishby

N

.

Opening the black box of deep neural networks via information

. arXiv preprint arXiv:170300810.

2017

.

https://doi.org/10.1016/j.jvcir.2016.11.003

73.

Kuo

CCJ

.

Understanding convolutional neural networks with a mathematical model

.

J Vis Commun Image R

.

2016

;

41

:

406

–

13

.

.

74.

Baral

C

,

Fuentes

O

,

Kreinovich

V

.

Why deep neural networks: a possible theoretical explanation

.

Constraint programming and decision making: theory and applications

.

Springer

;

2018

. p.

1

–

5

.

https://doi.org/10.1088/1742-5468/ab3985

75.

Saxe

AM

,

Bansal

Y

,

Dapello

J

,

Advani

M

,

Kolchinsky

A

,

Tracey

BD

,

On the information bottleneck theory of deep learning

.

J Stat Mech

.

2019

;

2019

(

12

):

124020

.

.

https://doi.org/10.1007/s11263-009-0275-4

76.

Everingham

M

,

Van Gool

L

,

Williams

CKI

,

Winn

J

,

Zisserman

A

.

The Pascal visual object classes (voc) challenge

.

Int J Comput Vis

.

2010

;

88

(

2

):

303

–

38

.

.

77.

Viola

P

,

Jones

M

.

Rapid object detection using a boosted cascade of simple features

. In:

Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001

.

IEEE

;

2001

.

Vol. 1

; p.

I

.

78.

Lienhart

R

,

Maydt

J

.

An extended set of haar-like features for rapid object detection

. In:

Proceedings International Conference on Image Processing

.

IEEE

;

2002

.

Vol. 1

. p.

I

.

79.

Dalal

N

,

Triggs

B

.

Histograms of oriented gradients for human detection

. In:

2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05)

;

2005

.

Vol. 1

; p.

886

–

93

.

80.

Felzenszwalb

PF

,

Girshick

RB

,

McAllester

D

.

Cascade object detection with deformable part models

. In:

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition

.

IEEE

;

2010

. p.

2241

–

8

.

81.

Scotti

F

.

Automatic morphological analysis for acute leukemia identification in peripheral blood microscope images

. In:

CIMSA. 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, 2005

.

IEEE

;

2005

. p.

96

–

101

.

https://doi.org/10.1016/j.compmedimag.2011.01.003

82.

Rezatofighi

SH

,

Soltanian-Zadeh

H

.

Automatic recognition of five types of white blood cells in peripheral blood

.

Comput Med Imaging Graph

.

2011

;

35

(

4

):

333

–

43

.

.

https://doi.org/10.1109/titb.2007.892694

83.

Theera-Umpon

N

,

Dhompongsa

S

.

Morphological granulometric features of nucleus in automatic bone marrow white blood cell classification

.

IEEE Trans Inf Technol Biomed

.

2007

;

11

(

3

):

353

–

9

.

.

84.

Theera-Umpon

N

.

White blood cell segmentation and classification in microscopic bone marrow images

. In:

International Conference on Fuzzy Systems and Knowledge Discovery

.

Springer

;

2005

. p.

787

–

96

.

85.

Chen

M

,

Xu

X

,

Zhang

J

,

Peng

X

,

Tang

G

,

Fengqi

F

,

Automatic bone marrow cell classification on digital images by an artificial intelligence (AI)-based system: a pilot study

. In:

Laboratory investigation

.

New York, NY

:

Nature Publishing Group

;

2019

.

Vol. 99

.

86.

Yosinski

J

,

Clune

J

,

Nguyen

A

,

Fuchs

T

,

Lipson

H

.

Understanding neural networks through deep visualization

. arXiv preprint arXiv:150606579.

2015

.

87.

Zeiler

MD

,

Fergus

R

.

Visualizing and understanding convolution networks

.

European Conference on Computer Vision

.

Springer

;

2014

. p.

818

–

33

.

88.

Shrikumar

A

,

Greenside

P

,

Kundaje

A

.

Learning important features through propagating activation diﬀerences

. In:

Proceedings of the 34th International Conference on Machine Learning

.

JMLR.org

;

2017

.

Vol. 70

; p.

3145

–

53

.

89.

Smilkov

D

,

Thorat

N

,

Kim

B

,

Viégas

F

,

Wattenberg

M

.

SmoothGrad: removing noise by adding noise

. arXiv preprint arXiv:170603825.

2017

.

90.

Adebayo

J

,

Gilmer

J

,

Muelly

M

,

Goodfellow

I

,

Hardt

M

,

Kim

B

.

Sanity checks for saliency maps

.

Adv Neural Inf Process Syst

;

2018

. p.

9505

–

15

.

91.

Greenspan

H

,

Van Ginneken

B

,

Summers

RM

.

Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique

.

IEEE Trans Med Imaging

.

2016

;

35

(

5

):

1153

–

9

.

https://doi.org/10.1016/j.neunet.2018.07.011

92.

Buda

M

,

Maki

A

,

Mazurowski

MA

.

A systematic study of the class imbalance problem in convolutional neural networks

.

Neural Netw

.

2018

;

106

:

249

–

59

.

.

https://doi.org/10.1186/s40537-019-0192-5

93.

Johnson

JM

,

Khoshgoftaar

TM

.

Survey on deep learning with class imbalance

.

J Big Data

.

2019

;

6

(

1

):

27

.

.

94.

Guan

MY

,

Gulshan

V

,

Dai

AM

,

Hinton

GE

.

Who said what: modeling individual labelers improves classification

. In:

Thirty-Second AAAI Conference on Artificial Intelligence

;

2018

.

https://doi.org/10.1162/neco.1989.1.3.295

95.

Barlow

HB

.

Unsupervised learning

.

Neural Comput

.

1989

;

1

(

3

):

295

–

311

.

.