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RSNA 肺炎コンペ6th Place Solution チーム PFNeumonia

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2018年12月1日 Kaggle Tokyo Meetup #5 LT 講演資料
平野湧一郎「RSNA Pneumonia Detection Challenge 6th Place Solution」
ソースコード:https://github.com/pfnet-research/pfneumonia

Published in: Technology
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RSNA 肺炎コンペ6th Place Solution チーム PFNeumonia

  1. 1. RSNA 肺炎コンペ 6th Place Solution チーム PFNeumonia Preferred Networks, Inc. Kaggle Tokyo Meetup #5 on Dec 1st.
  2. 2. 参加までの経緯  PFDetの活躍をすごいな〜と思って見ていた  新しく肺炎のコンペが開催されると聞く  しかも同じObject detection!!  ちょうど他のプロジェクトが一段落したところ
  3. 3. チームメンバー Motoki ABE Yuichiro HIRANO Keita ODA Yohei SUGAWARA Shuji SUZUKI Masashi YOSHIKAWA
  4. 4.  期間:8月末〜10月末  Stage 1: 7th  Stage 2: 7th 6th
  5. 5. 問題設定
  6. 6.  X線画像 (1x1024x1024) から 肺炎の位置を矩形で予測  Bounding-boxのラベルは 「肺炎」一種類のみ  画像全体のラベルは3種類  正常  正常でも肺炎でもない  肺炎 Ground-truthの一例
  7. 7. 8525 11500 5659 0 2000 4000 6000 8000 10000 12000 14000 正常 正常でも肺炎でもない 肺炎
  8. 8.  t = 0.4, 0.45, 0.5, ..., 0.75 それぞれに対し, IoU ≥ t なら TP (true positive)  各画像について以下の値を計算  ↑の平均がスコア
  9. 9. 我々の解法  基本はU-net風のsemantic segmentation  各ピクセルが  Bounding-box内かどうか (‘seg’)  肺炎/非肺炎の境界にあるかどうか (‘edge’) をそれぞれ予測する
  10. 10. What is ‘seg’ and ‘edge’ layer? Ground-truth Ideal ‘seg’ layer Ideal ‘edge’ layer
  11. 11. Input Image 3x512x512 64x256x256 256x128x128 512x64x64 1024x32x32 2048x16x16 1024 ResNet 152 ⊕ 512 ⊕ 256 ⊕ 128 ⊕ 64x256x256 Output vector ⊕ 3x3 conv, ReLU 2x Unpooling Concatenation U-net-like Backbone
  12. 12. 64x256x256 Output vector 1x256x256 ‘seg’ layer ⊕ 4x256x256 ‘edge’ layer 1x1 conv sigmoid 1x1 conv sigmoid 3x3 conv ReLU 3x3 conv ReLU Network Head Copy
  13. 13. Inference 縦に2分割 長方形を全通り試す ‘edge’ layerの 相乗平均の積が 最大のものを見つける p = ptop * pbottom * pleft * pright
  14. 14. Loss  Ledge : ‘edge’ layer の cross entropy loss  Lseg : ‘seg’ layer の F1値をf1としたとき,1 – f1  L = Lseg + α2 Ledge  α: ‘seg’ layer の pixel-wise accuracy
  15. 15. 解法に至った経緯  問題設定としてはobject detection  下記のような理由で,semantic segmentation ベースの解法を考え始めた  Bounding-boxのラベルが一種類  Bounding-boxどうしのoverlapがない  肺炎は一部を切り取っても肺炎 ←?
  16. 16. Not a 猫 肺炎
  17. 17. Semantic segmentation  出力サイズは最初16x16  Score 0.14〜0.16程度  他の方のアドバイスにより,  Unpoolingで解像度を復元  肺炎と非肺炎の境界を予測 したところ,LB 0.2まで上昇
  18. 18. 問題点① 計算量  長方形を全通り試す→愚直にやるとO(N4) アルゴリズム的に改良できないか?  →頑張って考えたが思いつかなかった しょうがないので愚直に4重ループ+枝刈り
  19. 19.  辺の候補をスコアの良い順に試す  それより先が最高でも最高記録に届かなければbreak
  20. 20. 問題点② ‘edge’のインバランス  ‘edge’ layerは,たとえ肺炎の画像でも ほとんどのpixelがnegative  → negative pixel の約99.8%を 学習時に無視
  21. 21. アンサンブル  Test time augmentation (x-flip) + 10-fold CV  LB 0.211 → 0.231
  22. 22. アンサンブル  testデータは,bounding-boxが被っていた場合 3人の医師のintersectionを取ったとのこと  これを真似して,10モデルを5+5にわけて, それぞれで予測してintersectionを最終予測にした  LB 0.236
  23. 23. 最終提出  90-degree rotation, zoom-in/outを追加  ResNet101 → ResNet152  10 epoch → 30 epoch  LB 0.236 → 0.241 (Stage1 7th)
  24. 24. 他の上位陣の解法
  25. 25. 1st Place  Classification + Detection  膨大なアンサンブル  全てのbboxを87.5%に縮小  LB 0.222 → 0.260!
  26. 26. アンサンブル  Classification: InceptionResNetV2, Xception, DenseNet169  Detection: RetinaNet, Deformable R-FCN, Deformable Relation Networks  TTAも膨大
  27. 27. 2nd Place  SE-ResNeXt101-RetinaNet  4-fold CV  Box sizeのアンサンブル時に, 小さめ(20パーセンタイル値)の値を採用
  28. 28. 3rd Place  RetinaNet  全てのbboxを83%に縮小 → 全員bboxを縮小している……
  29. 29. 重要な気付き  Bboxを縮小すると,スコアが増える  我々の解法でも,アンサンブル時にintersectionを 取ることでimplicitに小さくしていたが, 固定倍率で縮小するのは試していなかった  (まさかそんなに変わるとは思っていなかった)
  30. 30.  5+5でアンサンブルしてスコアが伸びたのも, おそらく単にbboxが小さくなったため  試しに10-CVで予測 → 90%に縮小したところ, 0.23478 → 0.24877 (2位!)
  31. 31. 反省点・感想  Discussionをあまり読んでいなかった  trainとtestの違い等に気付くのが遅れた  Bboxを縮小することを試さなかった  PFNの計算環境は神 (Tesla P100を常時8〜16台使っていた)
  32. 32. 閾値 t によるスコアの変化(Stage 1) • Adopted t = 0.3 (mAP = 0.24128) • Best t = 0.29794 (mAP = 0.24388)
  33. 33. 実装の詳細  Data augmentation: x-flip, 90-degree rotation, zoom-in/out, random contrast changes  3x oversampling of positive samples  Optimizer: Adam w/ weight decay 1e-4, 30 epochs  divide alpha by 10 after 20 & 27 epochs finish  Batch size: 10  Code: https://github.com/pfnet-research/pfneumonia

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