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Deep learning for medical imaging
- 1. Deep Learning for MedicalImagingGEETA CHAUHAN, CTO SVSG MARCH 5TH, 2018
- 2. Agenda Use casesfor Deep Learning in Medical Imaging What is Deep Learning? Deep Learning models in Medical Imaging Rise of Specialized Compute Techniques for Optimization E2E Pipeline Look into future Steps for starting your journey References
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- 4. Deep Learning inMedical Imaging Real-time Clinical Diagnostics (Enlitic) Whole-body Portable Ultrasound (Butterfly Networks, Baylabs) Radiology Assistant, Cloud Imaging AI (Zebra, Arterys) Intelligent Stoke Care (Viz.ai) Screening Tumor, Diabetic Retinopathy (Google, Enlitic, IBM) Oncology (Flatiron Health)
- 5. Source: Nature Skin Cancer 5.4M cases on non-melanoma skin cancer each year in US 20% Americans will get skin cancer Actinic Keratosis (pre-cancer) affects 58 M Americans 78k melanomas each year – 10K deaths $8.1B in US annual costs for skin cancer 5
- 6. Successes! Mammographic mass classification Brain Lesions Air way leakages Diabetic Retinopathy Prostrate Segmentation Breast cancer metastasis Skin Lesion Classification Bone suppression in Chest X-Rays 6 Source: arXiv:1702.05747
- 7. What is Deep Learning? AINeural Networks composed of many layers Learn like humans Automated Feature Learning Layers are like Image Filters
- 8. Deep Learning in Medical Imaging SURVEY OF300+ PAPERS 8 Source: arXiv:1702.05747
- 9. Medical imaging models Pre-trainednetworks with Transfer learning U-Net, V-Net, E-Net FCN – fully convolutional net with skip connections, Multi- stream CNNs TieNet, DenseCNN Encoder + RNN Decoder – Multi-label classification FCN + MDP (RL) for 2d/3d Image Registration 9 Source: arXiv:1505.04597
- 10. Medical imaging models 10 TIENET – AUTOMATICLABELS FOR CHEST X-RAYS
- 11. Shift towards SpecializedCompute Special purpose Cloud Google TPU, Microsoft Brainwave, Intel Nervana, IBM Power AI, Nvidia v100 Bare Metal Cloud – Preview AWS, GCE coming April 2018 Spectrum: CPU, GPU, FPGA, Custom Asics Edge Compute: Hardware accelerators, AI SOC Intel Neural Compute Stick, Nvidia Jetson, Nvidia Drive PX (Self driving cars) Architectures Cluster Compute, HPC, Neuromorphic, Quantum compute Complexity in Software Model tuning/optimizations specific to hardware Growing need for compilers to optimize based on deployment hardware Workload specific compute: Model training, Inference 11
- 12. CPU Optimizations LeverageHigh Performant compute tools Intel Python, Intel Math Kernel Library (MKL), NNPack (for multi-core CPUs) Compile Tensorflow from Source for CPU Optimizations Proper Batch size, using all cores & memory Proper Data Format NCHW for CPUs vs Tensorflow default NHWC Use Queues for Reading Data Source: Intel Research Blog 12
- 13. Tensorflow CPU Optimizations Compile from source git clone https://github.com/tensorflow/tensorflow.git Run ./configure from Tensorflow source directory Select option MKL (CPU) Optimization Build pip package for install bazel build --config=mkl --copt=-DEIGEN_USE_VML -c opt //tensorflow/tools/pip_package:build_pip_package Install the optimized TensorFlow wheel bazel-bin/tensorflow/tools/pip_package/build_pip_package ~/path_to_save_wheel pip install --upgrade --user ~/path_to_save_wheel /wheel_name.whl Intel Optimized Pip Wheel files 13
- 14. Parallelize your models Data Parallelism Tensorflow Estimator + Experiments Parameter Server, Worker cluster Intel BigDL Spark Cluster Baidu’s Ring AllReduce Uber’s Horovod TensorFusion HyperTune Google Cloud ML Model Parallelism Graph too large to fit on one machine Tensorflow Model Towers 14
- 15. Optimizations for Training Source:Amazon MxNET 15
- 16. Workload Partitioning Source: AmazonMxNET Minimize communication time Place neighboring layers on same GPU Balance workload between GPUs Different layers have different memory-compute properties Model on left more balanced LSTM unrolling: ↓ memory, ↑ compute time Encode/Decode: ↑ memory 16
- 17. Optimizations for Inferencing Graph Transform Tool Freeze graph (variables to constants) Quantize weights (20 M weights for IV3) Inception v3 93 MB → 1.5 MB Pruning, Weight Sharing, Deep Compression AlexNet 35x smaller, VGG-16 49x smaller 3x to 4x speedup, 3x to 7x more energy-efficient 17 bazel build tensorflow/tools/graph_transforms:transform_graph bazel-bin/tensorflow/tools/graph_transforms/transform_graph --in_graph=/tmp/classify_image_graph_def.pb --outputs="softmax" --out_graph=/tmp/quantized_graph.pb --transforms='add_default_attributes strip_unused_nodes(type=float, shape="1,299,299,3") remove_nodes(op=Identity, op=CheckNumerics) fold_constants(ignore_errors=true) fold_batch_norms fold_old_batch_norms quantize_weights quantize_nodes strip_unused_nodes sort_by_execution_order'
- 18. Cluster Optimizations Define yourML Container locally Evaluate with different parameters in the cloud Use EFS / GFS for data storage and sharing across nodes Create separate Data processing container Mount EFS/GFS drive on all pods for shared storage Avoid GPU Fragmentation problems by bundling jobs Placement optimizations – Kubernetes Bundle as pods, Mesos placement constraints GPU Drivers bundling in container a problem Mount as Readonly volume, or use Nvidia- docker 18
- 19. Uber’s Horovod on Mesos PeletonGang Scheduler MPI based bandwidth optimized communication Code for one GPU, replicates across cluster Nested Containers 19 Source: Uber Mesoscon
- 20. Pipeline: Google’s TFX 20 ContinuousTraining & Serving Data Analysis, Transformation, Validation Model Training, Validation, Serving Warm-Startup
- 21. Future: Explainability 21 Active researcharea Current Techniques Activation Heat Maps Saliency Maps Reconstruct Image t-sne vizualization
- 22. Future: FPGA HardwareMicroservices Project Brainwave Source: Microsoft Research Blog 22
- 23. FPGA Optimizations Brainwave CompilerSource: Microsoft Research Blog 23 Can FPGA Beat GPU Paper: ➢ Optimizing CNNs on Intel FPGA ➢ FPGA vs GPU: 60x faster, 2.3x more energy- efficient ➢ <1% loss of accuracy ESE on FPGA Paper: ➢ Optimizing LSTMs on Xilinx FPGA ➢ FPGA vs CPU: 43x faster, 40x more energy- efficient ➢ FPGA vs GPU: 3x faster, 11.5x more energy- efficient
- 24. Future: Neuromorphic Compute Intel’sLoihi: Brain Inspired AI Chip Neuromorphic memristors 24
- 25. Future: Quantum Computers Source: opentranscripts.org + PersonalizedMedicine for Cancer Treatment ? Cybersecurity a big challenge 25
- 26. Medical Imaging OpenDatasets http://www.cancerimagingarchive.net/ Lung Cancer, Skin Cancer, Breast Cancer…. Kaggle Open Datasets Diabetic Retinopathy, Lung Cancer Kaggle Data Science Bowl 2018 https://www.kaggle.com/c/data-science-bowl-2018 ISIC Skin Cancer Dataset https://challenge.kitware.com/#challenge/583f126bcad3a51cc66c8d9a Grand Challenges in Medical Image Analysis https://grand-challenges.grand-challenge.org/all_challenges/ And more… https://github.com/sfikas/medical-imaging-datasets 26
- 27. Where to startyour journey? Level 1: Just Starting Start with the Kaggle and other Open Competitions Use the existing pre-trained networks (like GoogleNet) with the Medical Open Source data Level 2: Intermediate Experiment with models specific to Medical Imaging space like U-Net/V-Net Combine 3rd party data sets for greater insights Level 3: Advanced Experiment with building new models from scratch Level 4: Mature Add feedback loop to your models, learning from outcomes Experiment with Deep Reinforcement Learning Industrialize the ML/DL Pipeline, shared model repository across company 27
- 28. Resources CBInsights AIin Healthcare Map: https://www.cbinsights.com/research/artificial-intelligence-startups-healthcare/ DL in Medical Imaging Survey : https://arxiv.org/pdf/1702.05747.pdf Unet: https://arxiv.org/pdf/1505.04597.pdf Learning to diagnose from scratch exploiting dependencies in labels: https://arxiv.org/pdf/1710.10501.pdf TieNet Chest X-Ray Auto-reporting: https://arxiv.org/pdf/1801.04334.pdf Dermatologist level classification of Skin Cancer using DL: https://www.nature.com/articles/nature21056 Tensorflow Intel CPU Optimized: https://software.intel.com/en-us/articles/tensorflow-optimizations-on-modern-intel- architecture Tensorflow Quantization: https://www.tensorflow.org/performance/quantization Deep Compression Paper: https://arxiv.org/abs/1510.00149 Microsoft’s Project Brainwave: https://www.microsoft.com/en-us/research/blog/microsoft-unveils-project-brainwave/ Can FPGAs Beat GPUs?: http://jaewoong.org/pubs/fpga17-next-generation-dnns.pdf ESE on FPGA: https://arxiv.org/abs/1612.00694 Intel Spark BigDL: https://software.intel.com/en-us/articles/bigdl-distributed-deep-learning-on-apache-spark Baidu’s Paddle-Paddle on Kubernetes: http://blog.kubernetes.io/2017/02/run-deep-learning-with-paddlepaddle-on- kubernetes.html Uber’s Horovod Distributed Training framework for Tensorflow: https://github.com/uber/horovod TFX: Tensorflow based production scale ML Platform: https://dl.acm.org/citation.cfm?id=3098021 Explainable AI: https://www.cc.gatech.edu/~alanwags/DLAI2016/(Gunning)%20IJCAI-16%20DLAI%20WS.pdf 28
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