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DL Hacks輪読
2017/02/03黒滝 紘生
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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
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3http://www.nipsml4hc.ws/posters , https://sites.google.com/site/icml2015mi/
ICML 2015 NIPS 2016
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4http://www.ibm.com/watson/health/index.html http://www.techrepublic.com/article/ibm-watsons-latest-gig-improving-cancer-treatment-wit
h-genomic-sequencing/
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5http://techon.nikkeibp.co.jp/atcl/event/15/063000072/071400009/?ST=health
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6https://www.elsevier.com/books/deep-learning-for-medical-image-analysis/zhou/978-0-12-810408-8
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Kaggle
7https://www.kaggle.com/c/data-science-bowl-2017
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目次
- CTスキャンによる3D肺画像
- X線の2D肺画像
- その他
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U-Net: Convolutional Networks for Biomedical Image Segmentation
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- 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)
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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
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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
-
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目次
- CTスキャンによる3D肺画像
- X線の2D肺画像
- その他
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Learning what to look in chest X-rays with a recurrent visual attention model
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- 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
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Learning what to look in chest X-rays with a recurrent visual attention model
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- Inception-v3に1%負けているが,パラメータ数は1/4で済む.
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Learning what to look in chest X-rays with a recurrent visual attention model
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- 左: 上がvalidationセットでの精度,下がattentionの頻度
- 右: attentionの進行
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Fully Convolutional Architectures for Multi-Class Segmentation in Chest Radiographs
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- 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
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Fully Convolutional Architectures for Multi-Class Segmentation in Chest Radiographs
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- 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)
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Deconvolutional Feature Stacking for Weakly-Supervised Semantic Segmentation
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- 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)
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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)
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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
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Medical image denoising using convolutional denoising autoencoders
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- 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)
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