An ENAS Based Approach for Constructing Deep Learning Models for - PowerPoint PPT Presentation

Jan 02, 2024 •479 likes •534 views

An ENAS Based Approach for Constructing Deep Learning Models for Breast Cancer Recognition from Ultrasound Images Mohammed Ahmed, Hongbo Du, Alaa AlZoubi School of Computing, University of Buckingham, UK {1200526, Hongbo.du, alaa.alzoubi}

An ENAS Based Approach for Constructing Deep Learning Models for Breast Cancer Recognition from Ultrasound Images Mohammed Ahmed, Hongbo Du, Alaa AlZoubi School of Computing, University of Buckingham, UK {1200526, Hongbo.du, alaa.alzoubi} @Buckingham.ac.uk MIDL 2020
Introduction Background • Widespread use of handcrafted CNN architectures (AlexNet, VGGNet, GoogLeNet ,ResNet …etc.) Framework of NAS technique • CNN architectures: hard to design • Recent Development: automatic architecture search ▪ Neural Architecture Search (NAS) ▪ Efficient Neural Architecture Search (ENAS) Aim of the Study To Investigate effectiveness of ENAS for breast cancer recognition from US images 2 MIDL 2020
Methods Hyperparameter setting for final CNN Model: Hyperparameter setting for Searching: • Batch size = 8 • Batch size = 8 • No. of epochs =100 Image size =100  100 • Image size =100  100 • • Other hyperparameter settings: the default • Other hyperparameter settings: the default values of ENAS values of ENAS Layers in final architectures: • ENAS 17 (5*N, R, 5*N, R, 5*N) • ENAS 7 (N, R, N, R, 3*N) Optimal cells(Norman and Reduction) generated by ENAS for Breast Cancer classification 3 MIDL 2020
Data and Results Dataset • Ultrasound images for breast lesions (262 Benign and 262 Malignant images) • Different US machine makes Examples of ultrasound images with labeled region of interest Data Preparation • Manual cropping of RoI by radiologist ENAS Models Performance and Comparison with Models of Other Architectures • Training data augmentation: Models TNR TPR PR Accuracy #Parameters ▪ Geometric Methods: Rotation (90,180 and ENAS 17 86.7% 92.0% 87.5% 89.3% 4,251,780 270), and Mirroring ENAS 7 90.9% 86.7% 91.0% 88.8% 2,342,484 ▪ Singular Value Decomposition (SVD) (25, 35 AlexNet 51.6% 48.5% 50.0% 50.0% 56,858,656 and 45) CNN3 [1] 80.5% 75.6% 84.0% 78.1% 619,202 • Bicubic Resizing (100  100) 4 MIDL 2020
Concluding Remarks • Conclusion : ▪ Investigated the efficacy of the ENAS approach for designing CNN architectures for breast lesion classification from US. ▪ Demonstrated that the ENAS technique reduces human interventions in CNN architecture design. ▪ The optimized architectures lead to more accurate classification yet simpler models than hand- crafted alternatives for breast lesion classification . • Future work: ▪ Evaluating ENAS models on external datasets ▪ Exploiting ENAS architectures for other types of tumors/lesions from ultrasound images MIDL 2020 5

Recommend

A Hybrid Deep Learning Approach For Chaotic Time Series Prediction Based On Unsupervised Feature

A Hybrid Deep Learning Approach For Chaotic Time Series Prediction Based On Unsupervised Feature Learning Norbert Ayine Agana Advisor: Abdollah Homaifar Autonomous Control & Information Technology Institute (ACIT), Departmenent of

379 views • 35 slides

A Deep Learning-based approach to VM behavior identification in cloud systems Matteo Stefanini,

A Deep Learning-based approach to VM behavior identification in cloud systems Matteo Stefanini, Riccardo Lancellotti, Lorenzo Baraldi, Simone Calderara University of Modena and Reggio Emilia Department of engineering Enzo Ferrari CLOSER

180 views • 16 slides

A deep learning based approach for genetic risk prediction Raquel Dias, PhD. Senior Staff

A deep learning based approach for genetic risk prediction Raquel Dias, PhD. Senior Staff Scientist Scripps Research Translational Institute raqueld@scripps.edu, @RaquelDiasSRTI Ali Torkamani, PhD. atorkama@scripps.edu, @ATorkamani Whole

1k views • 38 slides

A Component-based Approach for Constructing High-confidence Distributed Embedded Systems Barrett

A Component-based Approach for Constructing High-confidence Distributed Embedded Systems Barrett Bryant 1 , Rajeev Raje 2 , Mikhail Auguston 3 , 2 and Jeff Gray 1 , Shih-Hsi Liu 1 , Mihran Tuceryan Andrew Olson 2 1. University of Alabama at

382 views • 17 slides

TOWARDS CREATING A KNOWLEDGE GAP FOR DEEP LEARNING BASED MEDICAL IMAGE ANALYSIS Dr. S.

TOWARDS CREATING A KNOWLEDGE GAP FOR DEEP LEARNING BASED MEDICAL IMAGE ANALYSIS Dr. S. Kevin Zhou, Chinese Academy of Sciences Deep learning Input Algorithm: Output image variable Deep network X Y Y = f(X; W) Learning: arg min W S

685 views • 36 slides

Deep learning approach to describe and classify fungi microscopic images Medical Imaging with

Deep learning approach to describe and classify fungi microscopic images Medical Imaging with Deep Learning 2020 Bartosz Zieliski, Agnieszka Sroka-Oleksiak, Dawid Rymarczyk, Adam Piekarczyk, Monika Brzychczy-Woch Motivation We use a

181 views • 5 slides

Minjie Wang Deep Learning Deep Learning trend in the past 10 years Caffe State-of-art DL

Tofu: Parallelizing Deep Learning Systems with Automatic Tiling Minjie Wang Deep Learning Deep Learning trend in the past 10 years Caffe State-of-art DL system is based on dataflow GPU#0 w1 w2 data g1 g2 Forward propagation

405 views • 38 slides

Approach to practical application of Deep Learning in manufacturer's production line Masahiro

Approach to practical application of Deep Learning in manufacturer's production line Masahiro Kashiwagi, Hiroyuki Kusaka, Kiminori Kurosawa and Kenji Nishide 1 1 Applications Service Agriculture Advertisement Education Security Deep

504 views • 32 slides

CS7015 (Deep Learning) : Lecture 1 (Partial/Brief) History of Deep Learning Mitesh M. Khapra

CS7015 (Deep Learning) : Lecture 1 (Partial/Brief) History of Deep Learning Mitesh M. Khapra Department of Computer Science and Engineering Indian Institute of Technology Madras 1/49 Acknowledgements Most of this material is based on the

1.55k views • 98 slides

CS7015 (Deep Learning) : Lecture 1 (Partial/Brief) History of Deep Learning Mitesh M. Khapra

CS7015 (Deep Learning) : Lecture 1 (Partial/Brief) History of Deep Learning Mitesh M. Khapra Department of Computer Science and Engineering Indian Institute of Technology Madras 1/81 Acknowledgements Most of this material is based on the

923 views • 81 slides

Deep learning to diagnose the neutron star Kenji Fukushima The University of Tokyo Based on

Deep learning to diagnose the neutron star Kenji Fukushima The University of Tokyo Based on work with Yuki Fujimoto, Koichi Murase Deep Learning and Physics 2018 June 2, 2018 @ Osaka 1 Disclaimer I am a user of the deep

619 views • 24 slides

Deep Feedforward Networks Lecture slides for Chapter 6 of Deep Learning www.deeplearningbook.org

Deep Feedforward Networks Lecture slides for Chapter 6 of Deep Learning www.deeplearningbook.org Ian Goodfellow Last updated 2016-10-04 Roadmap Example: Learning XOR Gradient-Based Learning Hidden Units Architecture Design

483 views • 30 slides

Advanced Model-Based Reinforcement Learning CS 294-112: Deep Reinforcement Learning Sergey

Advanced Model-Based Reinforcement Learning CS 294-112: Deep Reinforcement Learning Sergey Levine Class Notes 1. Homework 3 is extended by one week, to Wednesday after next Todays Lecture 1. Managing overfitting in model-based RL

686 views • 40 slides

Towards Interpretable Deep Learning for Natural Language Processing Roy Schwartz University of

Towards Interpretable Deep Learning for Natural Language Processing Roy Schwartz University of Washington & Allen Institute for Artificial Intelligence December 2018 1 / 37 ( Deep-Learning -Based) AI Today 2 / 37 3 / 37 Deep Learning

1.63k views • 140 slides

Robust Deep Learning Based on Meta-learning Deyu Meng Xian Jiaotong University

Robust Deep Learning Based on Meta-learning Deyu Meng Xian Jiaotong University dymeng@mail.xjtu.edu.cn http://gr.xjtu.edu.cn/web/dymeng Deep Learning Robust Meta-learning The Success of Deep Learning Relies on

1k views • 46 slides

Hao Su July 6, 2017 Outline Overview of 3D deep learning 3D deep learning algorithms

Deep 3D Representation Learning for Visual Computing Hao Su July 6, 2017 Outline Overview of 3D deep learning 3D deep learning algorithms Conclusion 2 Outline Overview of 3D deep learning Background 3D deep learning tasks 3D deep

1.66k views • 122 slides

AMMI Introduction to Deep Learning 9.1. Transposed convolutions Fran cois Fleuret

AMMI Introduction to Deep Learning 9.1. Transposed convolutions Fran cois Fleuret https://fleuret.org/ammi-2018/ Fri Nov 9 22:39:08 UTC 2018 COLE POLYTECHNIQUE FDRALE DE LAUSANNE Constructing deep generative architectures

849 views • 46 slides

GPU-BASED A3C FOR DEEP REINFORCEMENT LEARNING M. Babaeizadeh , , I.Frosio , S.Tyree

GPU-BASED A3C FOR DEEP REINFORCEMENT LEARNING M. Babaeizadeh , , I.Frosio , S.Tyree , J. Clemons , J.Kautz University of Illinois at Urbana-Champaign, USA NVIDIA, USA An ICLR 2017 paper A github project GPU-BASED

1.16k views • 48 slides

ACCELERATE DEEP LEARNING WITH NVIDIA'S DEEP LEARNING PLATFORM | STEPHEN JONES | GTC16 DEEP

ACCELERATE DEEP LEARNING WITH NVIDIA'S DEEP LEARNING PLATFORM | STEPHEN JONES | GTC16 DEEP LEARNING EVERYWHERE INTERNET & CLOUD MEDICINE & BIOLOGY MEDIA & ENTERTAINMENT SECURITY & DEFENSE AUTONOMOUS MACHINES Image

308 views • 26 slides

Learning and Generating Distributed Routing Protocols Using Graph-Based Deep Learning Fabien

Chair of Network Architectures and Services Department of Informatics Technical University of Munich Learning and Generating Distributed Routing Protocols Using Graph-Based Deep Learning Fabien Geyer, Georg Carle Monday 20 th August, 2018 ACM

819 views • 28 slides

GTC 2018 Silicon Valley, California Predictive Learning of Factor Based Strategies using Deep

GTC 2018 Silicon Valley, California Predictive Learning of Factor Based Strategies using Deep Neural Networks for Investment and Risk Management Yigal Jhirad and Blay Tarnoff March 27, 2018 GTC 2018: Table of Contents I. Deep Learning in

340 views • 31 slides

Excitation Deep Learning Model Abdul Qayyum ImViA laboratory, University of Bourgogne

Medi dical cal Imagin ging g with th Deep ep Learni ning ng 2020 20 Short t Paper er MID IDL L 2020 20 Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning

203 views • 5 slides

Towards a learning approach for abbreviation detection and resolution Klaar Vanopstal, Bart

Background Annotation Pattern-based approach Learning-based approach Conclusions and future work Towards a learning approach for abbreviation detection and resolution Klaar Vanopstal, Bart Desmet, V eronique Hoste LT 3 , Language and

1.42k views • 84 slides

Paper number: 136 A deep learning approach to segmentation of the developing cortex in fetal

Paper number: 136 A deep learning approach to segmentation of the developing cortex in fetal brain MRI with minimal manual labeling AE Fetit , A Alansary, L Cordero-Grande, J Cupitt, AB Davidson, AD Edwards, JV Hajnal, E Hughes, K Kamnitsas, V

676 views • 11 slides