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Mammogram Image
Classification using Attention
Mechanism with Transfer
Learning
Dr. Munir Ahmad
Postdoc Software Engineering, University of Hertfordshire, UK
PhD Medical Physics (Imaging), University College London, UK
MSc Nuclear Engineering (Control Engineering), PIEAS, PK
MSc Physics (Electronics), Punjab University, PK
(munirahm@gmail.com)
Presentation Overview……..
 What is image classification ?
 Networks used for image classification ?
 Standard Network models ?
 Mammogram dataset used ?
 Transfer Learning ?
 Attention mechanism ?
 Our model ?
 Results ?
 Conclusions ?
Cats and Dogs Image Classification….
Neural Network Model
Image
Classification
using
CNN
A
Standard
CNN
Model
(VGG
-16) Visual Geometry Group
(Oxofrd University) 2014
A
Standard
CNN
Model
(ResNet50)
Pre-Trained versions of CNN
The ImageNet dataset
contains 14,197,122
annotated images according
to the WordNet hierarchy.
These images are from 1000
classes.
Attention
mechanism In the image below,
say we only need to
pay attention on this
Balloon….
We can simply
segment this balloon
and only work with it
instead of the rest of
the image by masking
the area with white….
Need
for
CAD
Model…..  Early diagnosis, better treatment
 Increased efficiency
 Increased accuracy
 To save radiologist time
 To save radiologist effort
 Socio economic effect – (screening)
 Save costs involved
 Etc..
CA Breast is the 2nd most
common cancer in the world
and in Pakistan almost 45% of
cancers are CA Breast. The best
screening diagnosis available is
the mammography.
MIAS Dataset…..
MIAS is a small dataset
Total number of images: 323
Malignant: 53
Benign: 63
Normal: 207
Data Augmentation & Pre-Processing….
Operation used for pre-processing
Dataset Split Method…..
Our
Model
with
Attention
Mechanism
Model Parameters used…..
Methodology……
True Positive: Positive as Positive (TrP)
False Positive: Positive as Negative (FaP)
True Negative: Negative as Negative (TrN)
False negative: Negative as Positive (FaN)
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =
𝑇𝑟𝑃 + 𝑇𝑟𝑁
𝑇𝑟𝑃 + 𝐹𝑎𝑃 + 𝑇𝑟𝑁 + 𝐹𝑎𝑁
× 100%
𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 =
𝑇𝑟𝑃
𝑇𝑟𝑃 + 𝐹𝑎𝑃
× 100%
𝑅𝑒𝑐𝑎𝑙𝑙 =
𝑇𝑟𝑃
𝑇𝑟𝑃 + 𝐹𝑎𝑁
× 100%
𝐹1 =
𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 × 𝑅𝑒𝑐𝑎𝑙𝑙
𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 + 𝑅𝑒𝑐𝑎𝑙𝑙
× 2
Precision can be seen
as a measure of
quality, and recall as a
measure of quantity.
Higher precision
means that an
algorithm returns more
relevant results than
irrelevant ones, and
high recall means that
an algorithm returns
most of the relevant
results (whether or not
irrelevant ones are
also returned).
Accuracy…with selected batch size…
Accuracy – with selected batch size…
Loss…..with selected batch size….
Loss…..with selected batch size….
Other Parameters…..
Conclusions
• Test and training accuracy and loss is better for VGG16 without
attention layer.
• ResNet50 performs better with attention layer for all malignant,
normal and benign cases.
• Similarly, test and training accuracy and loss is better for ResNet50
with attention layer.
• Feature extraction points need to be optimized – Future Work.
• Use of attention layer needs to be optimized – Future Work.
• For malignant cases, VGG16 perform equal with or without attention
layer for precision and recall.
Thank you……

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Transfer learning with attenuation mechanism for mammogram image.pptx

  • 1. Mammogram Image Classification using Attention Mechanism with Transfer Learning Dr. Munir Ahmad Postdoc Software Engineering, University of Hertfordshire, UK PhD Medical Physics (Imaging), University College London, UK MSc Nuclear Engineering (Control Engineering), PIEAS, PK MSc Physics (Electronics), Punjab University, PK (munirahm@gmail.com)
  • 2. Presentation Overview……..  What is image classification ?  Networks used for image classification ?  Standard Network models ?  Mammogram dataset used ?  Transfer Learning ?  Attention mechanism ?  Our model ?  Results ?  Conclusions ?
  • 3. Cats and Dogs Image Classification…. Neural Network Model
  • 5.
  • 6. A Standard CNN Model (VGG -16) Visual Geometry Group (Oxofrd University) 2014
  • 8. Pre-Trained versions of CNN The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. These images are from 1000 classes.
  • 9. Attention mechanism In the image below, say we only need to pay attention on this Balloon…. We can simply segment this balloon and only work with it instead of the rest of the image by masking the area with white….
  • 10. Need for CAD Model…..  Early diagnosis, better treatment  Increased efficiency  Increased accuracy  To save radiologist time  To save radiologist effort  Socio economic effect – (screening)  Save costs involved  Etc.. CA Breast is the 2nd most common cancer in the world and in Pakistan almost 45% of cancers are CA Breast. The best screening diagnosis available is the mammography.
  • 11. MIAS Dataset….. MIAS is a small dataset Total number of images: 323 Malignant: 53 Benign: 63 Normal: 207
  • 12. Data Augmentation & Pre-Processing…. Operation used for pre-processing
  • 16. Methodology…… True Positive: Positive as Positive (TrP) False Positive: Positive as Negative (FaP) True Negative: Negative as Negative (TrN) False negative: Negative as Positive (FaN) 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = 𝑇𝑟𝑃 + 𝑇𝑟𝑁 𝑇𝑟𝑃 + 𝐹𝑎𝑃 + 𝑇𝑟𝑁 + 𝐹𝑎𝑁 × 100% 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 = 𝑇𝑟𝑃 𝑇𝑟𝑃 + 𝐹𝑎𝑃 × 100% 𝑅𝑒𝑐𝑎𝑙𝑙 = 𝑇𝑟𝑃 𝑇𝑟𝑃 + 𝐹𝑎𝑁 × 100% 𝐹1 = 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 × 𝑅𝑒𝑐𝑎𝑙𝑙 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 + 𝑅𝑒𝑐𝑎𝑙𝑙 × 2 Precision can be seen as a measure of quality, and recall as a measure of quantity. Higher precision means that an algorithm returns more relevant results than irrelevant ones, and high recall means that an algorithm returns most of the relevant results (whether or not irrelevant ones are also returned).
  • 18. Accuracy – with selected batch size…
  • 22. Conclusions • Test and training accuracy and loss is better for VGG16 without attention layer. • ResNet50 performs better with attention layer for all malignant, normal and benign cases. • Similarly, test and training accuracy and loss is better for ResNet50 with attention layer. • Feature extraction points need to be optimized – Future Work. • Use of attention layer needs to be optimized – Future Work. • For malignant cases, VGG16 perform equal with or without attention layer for precision and recall.