This document discusses the use of artificial intelligence in breast imaging, specifically for the early detection of breast cancer. It provides background on common breast imaging techniques like mammography, tomosynthesis, ultrasound and MRI. It then discusses traditional CAD (computer-aided detection) systems and their limitations in detecting cancers. The document introduces artificial intelligence and how techniques like machine learning and deep learning can improve upon traditional CAD systems. It reviews several studies that have found AI-based systems can help radiologists achieve higher accuracy and reduce false-positive rates compared to unaided diagnosis. Finally, it mentions several companies developing AI solutions for applications in mammography, tomosynthesis and breast MRI.

1. ARTIFICIAL INTELLIGENCE IN BREAST IMAGING
504181416 Zeynep İrem Çakırca
ISTANBUL TECHNICAL UNIVERSITY
Biomedical Engineering MSc
Lecture : BME504E Medical Imaging Systems
Instructor : Prof. Dr. İnci Çilesiz
15 MAYIS 2019

2. 1
ABSTRACT
Artificial intelligence is gaining more attention in medical imaging nowadays. Artificial
intelligence is a branch of computer sciences that highlights the making of intelligent machines
behave like a human. Some methods of artificial intelligence are developed for classification,
image analysis, problem solving, learning, recognition of speech etc.[1].
Breast diseases and breast cancer is very common among women in the world. It is very
important to diagnose breast cancer at an early stage for prevention metastasis of cancerous
tumours and death according to cancer. Mammography is very popular imaging technique for
early diagnosis of breast cancer. Tomosynthesis is kinda mammography which gives 3D
information about breast [7]. And also ultrasound and breast MRI can be used as an adjunt to
mammography screening if necessary [2]. Sometimes it is very hard to detect either malignant
or benign cancer tissue of high dense breast. And sometimes even diagnosis is malignant, the
tissue may not be malignant. This situation cause overtreatment and unnecessary exposure of
radiation [2,3].
Reading of the breast image is very important to diagnose correctly. Sometimes even
experienced radiologists may make mistakes which causes overtreatment and increase in recall
rates. Computer-aided detection is traditionally used for diagnosis to help radiologists. There
are some challenges in CAD systems such as low signal-to-noise ratio in mammograms and
differences in physical properties of lesions and also detection with false-positives that causes
unnecessary treatments [2]. To improve efficiency of traditional CAD systems, artificial
intelligence could be used such as machine learning, deep learning (artificial neural networks
mostly). AI based CAD systems provide decrease in reading time but it is said that the system
alone is not superior to radiologists [3]. When it is used with experienced radiologists, the
results are more promising. In this study, brief information about how artificial intelligence
methods are used in breast imaging and their efficiency for improving the diagnose were given.

3. 2
TABLE OF CONTENTS
1. Introduction ............................................................................................................................ 3
1.1. Mammography................................................................................................................ 3
1.2. Digital Breast Tomosynthesis......................................................................................... 4
1.3. Breast Magnetic Resonance Imaging (MRI) and Ultrasound ....................................... 4
2. CAD (Computer Aided Detection) Systems in Mammography ............................................ 5
2.1 Limitations of CAD Systems ........................................................................................... 6
3. What is Artificial Intelligence ? ............................................................................................. 6
4. AI Systems in Breast Imaging................................................................................................ 7
4.1 Studies About AI and CAD Systems In Breast Imaging .................................................. 9
4.2 Companies for AI In Breast Imaging................................................................................ 9
5. Conclusion............................................................................................................................ 10
REFERENCES......................................................................................................................... 11

4. 3
1. INTRODUCTION
Today the breast cancer is very problematic topic which is the most common cancer type
among women. Early detection is this cancer is very important because in earlier stages of
cancer, it is easier to return patient to healthy state.
In some cases, breast ultrasound and MRI can be used to image breast tissues but still
mammography is superior. Performance of experienced radiologists is intermediate to
detect cancer at heterogeneosly or high dense breasts due to the properties of
mammography. Sometimes there are overdiagnoses which means that finding abnormalities
that match cancer pathologically but never show symptoms or progress death and false-
positive diagnosis as a result of these problems and causes unnecessary biopsies or
treatments [3]. To improve efficiency of detection, computer-aided detection and artificial
intelligence systems are used [2].
1.1. Mammography
Mammography is a radiological method which uses low energy X-rays to screen breasts.
Mammogram has mainly X-ray tube, compression paddle and detector. It works like other
X-ray imaging methods. X-ray images are generated due to different absorption rate of X-
rays by different type of tissues.
Figure 1. The components of mammography [4].
During imaging with mammography, breasts are compressed by paddles. This
compression compensate the breast tissue thickness to improve quality of images by
reducing the thickness which x-rays penetrate, reducing the scattered radiation that causes
reduction of image quality, decrease in radiation dose, and for prevention of blurring due to
motion, the breast are holded immobile. In mammographic imaging, the breast images are
taken from both head-to-foot (craniocaudal, CC) view and angled side-view (mediolateral
oblique, MLO) [5]. There are analog and digital mammography. Analog mammography
was typically applied by using screen-film cassettes. In digital mammography, digital
receptors and computers are used instead of x-ray film to help investigation of breast tissues.

5. 4
Digital mammography is also called full-field digital mammography (FFDM). Its system
is like the systems of digital cameras and it provide better images with lower dose. The
images of breast are send to a computer for examination by the radiologists and for furhter
storage. The procedure of taking images with digital mammogram is similar screen-film
mammograms [6].
1.2. Digital Breast Tomosynthesis
Digital breast tomosynthesis (DBT) is a newer method for imaging of breast. It is a form
of mammography which give 3-D images, and also called three-dimensional
mammography. DBT takes multiple images of the breast from different angles and
reconstructs or synthesizes into 3-D image sets. It is very similar to computed tomography
(CT) that uses thin slices are combined together to make a 3-D reconstruction of the body.
DBT uses slightly higher radiation dose than the standard mammography. Most studies
have indicated that imaging with DBT as an adjunct to standard mammogram, results in
better breast cancer detection rates and lower recall rates for additional testing. DBT
provides earlier detection of small breast cancers which could be hidden on a standard
mammogram, lower unnecessary biopsies or extra testing, obvious screening of
abnormalities within dense breasts, greater accuracy of physical features of lesions [6].
Figure 2. The illustration of digital breast tomosynthesis [7].
1.3. Breast Magnetic Resonance Imaging (MRI) and Ultrasound
Breast magnetic resonance imaging is preferred as an adjunctive to mammography for
patient who has high-risk to be cancer when mammographic images are insufficient. MRI
has some advantages such as non-ionising radiation and especially high sensitivity[8].
Ultrasound is another technique which is used for imaging of breast for gaining additional
information to read mammographic results abnormalities and it guide procedures. If a
woman has high breast density, breast ultrasound would help to detect cancer at an early
stage [2,8].

6. 5
Both breast MRI and US have computer-aided detection systems which is very similar with
mammographic CAD system. Also artificial intelligence methods are very useful for
interpreting breast MRI and US images. Generel information about these systems will be
given next parts of this report.
2. CAD (Computer Aided Detection) Systems in Mammography
Interpretation of various patterns in mammographic images is challenging and high level of
experience is necessary. CAD provides viewing of overlooked findings in radiological
examination by radiologists. The system search for abnormalities of density, mass,
calcification, shape which would be a sign of cancer. Mainly, the system does detection,
segmentation, classification of lesion [2].
Figure 3. Scheme of CAD systems in mammography [9].
Figure 4. Mammogram of ImageChecker ® CAD system [10].
It was said “The first commercial CAD system, ImageChecker M1000 system (R2
Technology, Los Altos, CA, USA), gained Food and Drug Administration (FDA) approval
in 1998 with the aim of providing a prompt or second opinion to the radiologist in order to
enhance radiologists’ diagnostic accuracy.” by Huang et al.[2].

7. 6
The R2 technology used traditional computer vision techniques for pattern recognition. It
searched for stack of highlighted spots, which would be signs of microcalcifications, and
radial lines which may be signs of spiculated masses within a concentric ring shapes
between 3 and 16 mm radii. The R2 system calculated the probability of microcalcifications
or masses are being malignant, and highlighted regions above a specified threshold of
likelihood for radiologists [11].
As shown in Figure 4, CAD systems highlights the region of interest and radiologist would
focuse more on these highlighted areas to find abnormalities.
In traditional CAD systems, machine learning classifier methods are mostly used. In these
methods, extracted features such as shape, location, size, calcifications are firstly
determined for describing to be malignancy. It is hard to determine these features and how
they are combined to obtain various outputs [2].
2.1. Limitations of CAD Systems
CAD systems are routinely used for decades but still have some restrictions. The systems
have some limitations due to low signal-to-noise ratio and various locations, shapes,
physical appearances of lesions. Also traditional CAD systems follow rule-based approach
so large amount of data are necessary to adjust the algorithms. Sometimes the system
increases the recall rates which means that additional examination of patients because of
larger number of false positives [2,12].
3. What is Artificial Intelligence ?
Figure 5. Branches of artificial intelligence [13].
Artificial intelligence is a field of computer science which include the creation of intelligent
machines that behave like a human. Some applications of artificial intelligence are speech
recognition, planning, problem solving, decision making, learning, classification etc.. AI
supports CAD for prevention of restrictions of traditional CAD systems.

8. 7
In this report, machine learning methods will be described further paragraphs because in
mammographic imaging methods, mostly machine learning methods such as deep learning,
artificial neural networks are used with AI based CAD systems [2].
4. AI Systems in Breast Imaging
Machine learning is the training of a pattern which learns the extracted features and
important parameters that are most relevant to observed data. Deep learning is a sub branch
of machine learning which is based on neural networks by mimicing neurons of a human
brain. The purpose of machine learning is to make estimations from previous observed data
by using cost function (e.g.,sigmoid function) to calculate the gap between current
estimations against a benign density or normal tissue. The pattern will adapt to decrease this
gap by further training until the estimated label is compatible with the ground truth that is
made by radiologists [2,3].
There are several deep learning methods published in the literature but most of them are
based on similar and simple artificial neural networks. An artificial neural network has an
input layer which can be raw pixels of mammograms, hidden layer or layers, an output layer
which can be probability of malignancy or benignancy [2,14].
Figure 6. The diagram of artificial neural network for mammography [14].
As shown with the diagram on figure 6, by adjusting the weight with transfer or cost
function, the algorithm reaches the most valuable output by using previous information as
well. There are two approach of using ANNs as an adjunct in mammography reading. The
first method is applying of the classifier directly to the region of interest (ROI). As a second
method, ANNs may learn from the extracted features from the previous image signals.
Mainly second method is used to decrease false-positive rates in detection of
microcalcifications [14].

9. 8
Also convolutional neural network systems (CNNs) which is a form of ML are very useful
algorithms in general image analysis. There is special layer which is called convolutional
layer in that convolution operation(kernel) behaves like a filter on the pixel matrix to find
related features of the input data abd so propose some shift invariance in CNNs. A linear
output which is also called feature map is created in convolutional layer. The pooling and
fully connected layers are other layers of this network. After the generation of feature map
in the convolutional layer, it is firstly passed through a non-linear cost function(e.g., a
rectified linear unit) that turns all negative values to zero, mapping it to an output. And then,
it is then passed into the pooling layer to provide down-sampling of the map. Finally, for
classification of whole outcome, the output is transferred to the fully connected layer. These
layers form components of the hidden layers of a CNN. If the number of the layers are
increased, feature extraction will become more complicated [2,3,17].
Figure 7. The matrix representation of measurements in convolutional layer [18].
Figure 8. The pooling layer of CNNs [18].

10. 9
Figure 9. An example of AI based CAD system mammographic images : “Lunit Insight
for Mammography is a diagnostic support tool that accurately detects malignant lesions
suspicious of breast cancer with 97 percent accuracy.”[19]
4.1. Studies About AI and CAD Systems In Breast Imaging
In a study that is carried out by Jieng et al. [15] just first description of clusters of
microcalcification was done by radiologists. The artificial neural networks that was tested
against five radiologists without support, described 100% of the malignant and 82% of the
benign cases . The accuracy was a little higher with ANN support (𝑃 = 0.03).
Becker et al. [16] showed the effects of ML algorithms on the mammographic screening.
Testing artificial neural networks (ANNs) against three readers on 18 cancers and 233
controls produced a sensitivity of 73.7% vs 66.6% and specificity of 72% vs 92.7%
respectively.
Chougrada et al. [17] applied pre-defined convolutional neural networks to select benign
lesions from malignants. Area under the receiver operating characteristic curve (AUC)
which shows the effect of a parameter to distinguish between two diagnostic groups was
0.99 when tested on an independent dataset of 113 mammograms with a radiologist pre-
definitions.
4.2. Companies for AI In Breast Imaging
There are several companies which work on the usage of artificial intelligence in breast
imaging. Even if the companies are mainly about CAD systems with AI in mammogram,
some of them about breast MRI and some of them are only for digital breast tomosyntheis.
Some examples of these companies are :
 Kheiron Medical Technologies
 Transpara by ScreenPoint Medical
 CureMetrix – CAD THAT WORKS®
 The Digital Mammography DREAM challenge(November 2016 to May 2017)
 Therapixel
 VolparaDensity and Quantra 2.2 Breast Density Assessment by Hologic (For breast
density)
 iCAD’s PowerLook Tomo Detection (for tomosynthesis)

11. 10
 Google (Artificial Intelligence Based Breast Cancer Nodal Metastasis Detection /
Impact of Deep Learning Assistance on the Histopathologic Review of Lymph
Nodes for Metastatic Breast Cancer, which was published in the The American
Journal of Surgical Pathology )
 MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL)
 Lunit Insight for Mammography
5. Conclusion
Development of AI systems in breast imaging is very complicated so high experienced
radiologists are necessary to put correct definitions about breast tissue into the machine
learning algorithms. According to most studies in literature, AI work effectively with
interpretation of radiologists. Sometimes some important lesions are overlooked by
radiologists, AI based CAD systems prevent to miss these overlooked lesions when it is
necessary. AI in breast imaging reduces recall rates by decreasing false-positive numbers,
so it prevent over-treatment and unnecessary biopsies. Generally, specifity is lower in AI
systems. For further investigation, increase in specificity would be provided [2,16]. There
are a lot of work to optimize these AI-CAD systems in breast imaging.

12. 11
REFERENCES
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Deep learning in mammography: diagnostic accuracy of a multipurpose image analysis
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