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DL Hacks輪読 2017/02/03 黒滝 紘生

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Page 1: [DL輪読会]Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation (NIPS 2016 Poster)／U-Net: Convolutional Networks for Biomedical Image

DL Hacks輪読

2017/02/03黒滝 紘生

Page 2: [DL輪読会]Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation (NIPS 2016 Poster)／U-Net: Convolutional Networks for Biomedical Image

趣旨

- 医用画像に適用されるDeep Learning

- タスク

- X線の2D肺画像

- CTスキャンによる3D肺画像

- その他

2

Page 3: [DL輪読会]Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation (NIPS 2016 Poster)／U-Net: Convolutional Networks for Biomedical Image

3http://www.nipsml4hc.ws/posters , https://sites.google.com/site/icml2015mi/

ICML 2015 NIPS 2016

http://www.nipsml4hc.ws/posters
http://www.nipsml4hc.ws/posters
Page 4: [DL輪読会]Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation (NIPS 2016 Poster)／U-Net: Convolutional Networks for Biomedical Image

4http://www.ibm.com/watson/health/index.html http://www.techrepublic.com/article/ibm-watsons-latest-gig-improving-cancer-treatment-wit

h-genomic-sequencing/

Page 5: [DL輪読会]Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation (NIPS 2016 Poster)／U-Net: Convolutional Networks for Biomedical Image

5http://techon.nikkeibp.co.jp/atcl/event/15/063000072/071400009/?ST=health

Page 6: [DL輪読会]Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation (NIPS 2016 Poster)／U-Net: Convolutional Networks for Biomedical Image

6https://www.elsevier.com/books/deep-learning-for-medical-image-analysis/zhou/978-0-12-810408-8

Page 7: [DL輪読会]Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation (NIPS 2016 Poster)／U-Net: Convolutional Networks for Biomedical Image

Kaggle

7https://www.kaggle.com/c/data-science-bowl-2017

Page 8: [DL輪読会]Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation (NIPS 2016 Poster)／U-Net: Convolutional Networks for Biomedical Image

目次

- CTスキャンによる3D肺画像

- X線の2D肺画像

- その他

8

Page 9: [DL輪読会]Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation (NIPS 2016 Poster)／U-Net: Convolutional Networks for Biomedical Image

U-Net: Convolutional Networks for Biomedical Image Segmentation

9

- https://arxiv.org/abs/1505.04597 , MICCAI 2015- 医用画像によく出てくる，細胞レベルの画像に適したCNNの構造を提案

- 現在のKaggleの3D 肺スキャン問題のTutorialに使われている (web)

https://arxiv.org/abs/1505.04597
https://arxiv.org/abs/1505.04597
Page 10: [DL輪読会]Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation (NIPS 2016 Poster)／U-Net: Convolutional Networks for Biomedical Image

Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation

10

- https://arxiv.org/abs/1609.01006 , NIPS2016 Poster , Cited by 3 (Google Scholar, Jan 23, 2017)- 3次元医療データでよく見られる異方性の性質を，LSTMによりうまく扱えている

- 異方性 = z軸方向だけ，xy平面と長さのスケールが違い，単純なCNNでは扱いにくい

- xy平面用のU-Netを複数つないだ出力を，z軸処理用のLSTMに投げている

Page 11: [DL輪読会]Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation (NIPS 2016 Poster)／U-Net: Convolutional Networks for Biomedical Image

Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation

11

- https://arxiv.org/abs/1609.01006 , NIPS2016 Poster , Cited by 3 (Google Scholar, Jan 23, 2017)- 3次元医療データでよく見られる異方性の性質をうまく扱えている

- xy平面用のU-Netを複数つないだ出力を，z軸処理用のLSTMに投げている

-

Page 12: [DL輪読会]Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation (NIPS 2016 Poster)／U-Net: Convolutional Networks for Biomedical Image

目次

- CTスキャンによる3D肺画像

- X線の2D肺画像

- その他

12

Page 13: [DL輪読会]Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation (NIPS 2016 Poster)／U-Net: Convolutional Networks for Biomedical Image

Learning what to look in chest X-rays with a recurrent visual attention model

13

- NIPS 2016 Workshop on Machine Learning for Healthhttp://arxiv.org/abs/1701.06452

- AttentionとConvolutional Autoencoderで，胸部X線から 心臓肥大と(埋め込みの)医療機器を検出．

- "Recurrent Models of Visual Attension (NIPS 2014)"を使っている

Page 14: [DL輪読会]Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation (NIPS 2016 Poster)／U-Net: Convolutional Networks for Biomedical Image

Learning what to look in chest X-rays with a recurrent visual attention model

14

- Inception-v3に1%負けているが，パラメータ数は1/4で済む．

Page 15: [DL輪読会]Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation (NIPS 2016 Poster)／U-Net: Convolutional Networks for Biomedical Image

Learning what to look in chest X-rays with a recurrent visual attention model

15

- 左: 上がvalidationセットでの精度，下がattentionの頻度

- 右: attentionの進行

Page 16: [DL輪読会]Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation (NIPS 2016 Poster)／U-Net: Convolutional Networks for Biomedical Image

Fully Convolutional Architectures for Multi-Class Segmentation in Chest Radiographs

16

- https://arxiv.org/abs/1701.08816 , Jan. 30 2017- 肺，鎖骨，心臓の検出をセグメンテーション問題として解いた

- JSRTという，247枚の画像のデータセットで学習

https://arxiv.org/abs/1701.08816
https://arxiv.org/abs/1701.08816
Page 17: [DL輪読会]Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation (NIPS 2016 Poster)／U-Net: Convolutional Networks for Biomedical Image

Fully Convolutional Architectures for Multi-Class Segmentation in Chest Radiographs

17

- U-Net という医療系のCNN亜種(後述)を，3つの方法で拡張

- a) All-Dropout : 全てのConvレイヤーの直後にDropout- b) InvertedNet : (a)のフィルタサイズを逆転

- c) All-Convolutional : poolingをConvで置き換えた

- 最終的に，(b)が良い性能を出した(下図の青)

Page 18: [DL輪読会]Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation (NIPS 2016 Poster)／U-Net: Convolutional Networks for Biomedical Image

Deconvolutional Feature Stacking for Weakly-Supervised Semantic Segmentation

18

- https://arxiv.org/pdf/1602.04984v3.pdf- 画像単位の教師ラベルしかないときに，ピクセル毎のセグメンテーションを出力する(weakly-supervised)- Deconvolutionした画像を全部合わせて，また識別器に入力する

- (論文の結果)

https://arxiv.org/pdf/1602.04984v3.pdf
https://arxiv.org/pdf/1602.04984v3.pdf
Page 19: [DL輪読会]Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation (NIPS 2016 Poster)／U-Net: Convolutional Networks for Biomedical Image

Self-Transfer Learning for Fully Weakly Supervised Object Localization

19

- https://arxiv.org/pdf/1602.01625v1.pdf- 前ページの論文の進化版で，画像が少なくpretrainingが難しいときでも使える

- 全体用のclassificationレイヤーと，ピクセルのlocalizationレイヤーを同時に学習する(だんだんlocalを増やす)- (論文の結果)

Page 20: [DL輪読会]Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation (NIPS 2016 Poster)／U-Net: Convolutional Networks for Biomedical Image

目次

- CTスキャンによる3D肺画像

- X線の2D肺画像

- その他

20

Page 21: [DL輪読会]Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation (NIPS 2016 Poster)／U-Net: Convolutional Networks for Biomedical Image

Medical image denoising using convolutional denoising autoencoders

21

- https://arxiv.org/pdf/1608.04667v2.pdf- 医用画像のノイズを取り除くのには，Convolutional DAEが有用

- (論文の結果)

Page 22: [DL輪読会]Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation (NIPS 2016 Poster)／U-Net: Convolutional Networks for Biomedical Image

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