The Utility of Deep Learning in Breast Ultrasonic Imaging: A Review.

Tomoyuki Fujioka,Mio Mori,Yoshio Kitazume,Yuka Yashima,Emi Yamaga,Miyako Nara,Tsuyoshi Nakagawa,Kyoko Nomura,Ukihide Tateishi,Kazunori Kubota,Leona Katsuta,Jun Oyama,Goshi Oda

doi:10.3390/diagnostics10121055

Abstract

Breast cancer is the most frequently diagnosed cancer in women; it poses a serious threat to women’s health. Thus, early detection and proper treatment can improve patient prognosis. Breast ultrasound is one of the most commonly used modalities for diagnosing and detecting breast cancer in clinical practice. Deep learning technology has made significant progress in data extraction and analysis for medical images in recent years. Therefore, the use of deep learning for breast ultrasonic imaging in clinical practice is extremely important, as it saves time, reduces radiologist fatigue, and compensates for a lack of experience and skills in some cases. This review article discusses the basic technical knowledge and algorithms of deep learning for breast ultrasound and the application of deep learning technology in image classification, object detection, segmentation, and image synthesis. Finally, we discuss the current issues and future perspectives of deep learning technology in breast ultrasound.

Highlights

Breast cancer is the most common cancer and the second leading cause of cancer-related death in women [1]
Cao et al studied the existing state-of-the-art CNN methods (Fast R-CNN, Faster R-CNN, YOLO, and SCSaDo) feotr ablr.eastsut dleiseidonthdeeteecxtiisotninugsisntgatber-eoafs-tthuel-tarartsoCuNndNimmaegtehso. dTshe(yFacsotllRec-tCedNaNd, aFtaassetetrcoRn-sCisNtiNng, YofO5L7O9,baenndigSnSaDn)dfo4r64brmeaaslitglnesainotnledseitoencstiaonndussuinbgmbirtteeadstthueltrualstroausnodunimd aimgeasg. eTshteoymcoalnleucatledanandoatatatisoent cboynesxispteinrigenocfe5d79clibneinciiagnnsa. nTdhe4y64fomunadligtnhaant Yt lOesLiOonasnadndSSsDubpmeriftotermd tshiegnuilfitrcaasnotulynbdeitmtear gthesantothmeaontuhearl amnentohtoadtisoinn bdyeteexcptienrgiebnrceeadstclleisniiocniasn[s4.6T].hey found that YOLO and Single Shot MultiBox Detector (SSD) perform significantly better than the other methods in detecting breast lesions [46]
generative adversarial networks (GANs) is a special type of neural network model in which two networks are trained simultaneously, with one focusing on image generation and the other focusing on discrimination

Summary

Introduction

Breast cancer is the most common cancer and the second leading cause of cancer-related death in women [1]. The use of breast ultrasound is increasing, and radiologists and clinicians spend significant time examining huge volumes of breast ultrasonic images. This has become a major problem in many countries because it leads to increased medical costs and worsens the patient case. Artifificial intelligence (AI), especially deep learning methods, has accomplished outstanding performance in automatic speech recognition, image recognition, and natural language processing. Deep learning has been used in breast ultrasonic imaging and will have a great infflluence in the future. Deep learning has dramatically improved research in areas such as speech recognition, visual image recognition, and object detection [24]. We must collect datasets (training, validation, and test data) to perform deep learning. GimoaogeLseNtoet ttroadinistaingdueeisph tnheeumraallingnetawncoyrkofwbritehast31m5a4ssmesaolingnualtnrat saonudnd4. 2T5h4eybreenpigonrtesdamthpatleths eudseinepg lGeaorongiLngeNmeotdteol dhaisdtiannguaicschurtahceymofa9li1g%n,aancsyenosfitibvrietyasotfm86a%ss,eas sopnecuifiltcriatysooufn9d3.%T,haenyd raenpaorretaedunthdaetr the cduerevpel(eAarUnCin)gofm>o0d.9el[3h2a]d. an accuracy of 91%, a sensitivity of 86%, a specificity of 93%, and an area under the curve (AUC) of >0.9 [32]

Result

Image synthesis

Findings

Discussion

Conclusions