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![研究室プロジェクト:
「いつでも」「どこでも」診断可能なオンラインベースでの
高精度自動診断システムを画像認識の技術を用いて構築[1]
3研究背景
研究背景
母斑 or メラノーマ？
メラノーマと診断しました。
[1] H.Iyatomi, H.Oka, M.Tanaka et al, “Quantization of clinical items of dermoscopy for computer-based melanoma diagnosis”,
22nd Fuzzy System Symposium, Sapporo, Sept. 6-8, 2006.](https://image.slidesharecdn.com/interim-181003080717/85/-4-320.jpg)
![4研究背景
本研究の目的
メラノーマ識別精度の向上
? 母斑/メラノーマの区別
?畳み込みニューラルネットワーク
?Fine-tuningによる転移学習
?形状に着目した前処理
?診断根拠の提示[2]
先行研究
?セグメンテーション
?アンサンブル学習
?半教師あり学習
手法の改良
[2] S.Murabayashi, H.Iyatomi, “Automated melanoma diagnosis and feasibility on presentation of its evidence with deep
transfer learning” in The Institute of Electronics, Information and Communication Engineers, 2018.](https://image.slidesharecdn.com/interim-181003080717/85/-5-320.jpg)
![6先行研究
先行研究: 畳み込みニューラルネットワークによる学習 [3]
従来手法: ハンドメイドによる複雑な処理を伴う学習
特徴量抽出 識別入力 出力
ハンドメイド 機械学習
特徴量抽出 & 識別入力 出力
畳み込みニューラルネットワーク
特徴量さえも機械に抽出させることで識別精度が向上
[3] A.Krizhevsky, I.Sutskerver, GE.Hinton, “ImageNet Classification with Deep Convolutional Neural Networks”, In Proc. NIPS, 2012.
畳み込みニューラルネットワーク](https://image.slidesharecdn.com/interim-181003080717/85/-7-320.jpg)
![7先行研究
前半: 事前学習済みモデルのパラメータを利用
後半: ターゲット?タスクで再学習 [4]
[4] R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,”
in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2014.
重みの転移
固定 再学習
学習済みモデル
Fine-tuning による転移学習](https://image.slidesharecdn.com/interim-181003080717/85/-8-320.jpg)
![8先行研究
母斑とメラノーマには形状の差異がある
?母斑: 一定の秩序を保ちゆっくり成長 ? 左右対称
?メラノーマ: 無作為かつ盛んに成長 ? 左右非対称
前処理で、腫瘍領域の長軸位置合わせを行うことで
差異を顕著化し、特徴量抽出をサポート[5]
[5] T.Yoshida, H.Iyatomi, “Alignment of Major Axis for Automated Melanoma Diagnosis with Deep Learning Approach”
in 31st Fuzzy System Symposium, September 2015.
母斑 メラノーマ
形状に着目した前処理](https://image.slidesharecdn.com/interim-181003080717/85/-9-320.jpg)
![データセット:
母斑: 3,777例 メラノーマ: 824例 （計4,601例）
train : test = 4 : 1
モデル:
VGG16（ImageNetで学習済み）をベース[6]
全結合層のみを再学習
学習パラメータ:
学習率: 0.001 学習回数: 70 バッチサイズ: 20
10実験
実験
目的
長軸位置合わせの有無による識別精度の変化を検証
[6] K.Simonyan, A.Zisserman, “VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION” Published as a
conference paper at ICLR 2015](https://image.slidesharecdn.com/interim-181003080717/85/-11-320.jpg)
![考察と今後の課題
15
?識別精度 皮膚科専門医[7]: 75?84% 識別器: 85.6%
? 長軸位置合わせはメラノーマ識別に有効
?転移学習のベースモデルにメラノーマは含まれないが高い成果
? 画像の汎用的な特徴を効率良く学習
?学習においては適切なパラメータチューニングが必要
?識別精度向上を目的とした画像処理やネットワークの改良
? セグメンテーション/アンサンブル学習等の検討
?ISIC2018コンペティションで使用された
データセット「HAM10000」への挑戦
? 体毛除去等に必要な知識の習得
考察と今後の課題
[7] Argenziano G, Soyer HP, Chimenti S et al: Dermoscopy of pigmented skin lesions: Results of a consensus meeting via the Internet.
Journal of Academy of Dermatology, 2003](https://image.slidesharecdn.com/interim-181003080717/85/-16-320.jpg)
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深层学习を用いたメラノーマ识别器の基础検讨
- 4. 研究室プロジェクト: 「いつでも」「どこでも」診断可能なオンラインベースでの 高精度自動診断システムを画像認識の技術を用いて構築[1] 3研究背景 研究背景 母斑 or メラノーマ？ メラノーマと診断しました。 [1] H.Iyatomi, H.Oka, M.Tanaka et al, “Quantization of clinical items of dermoscopy for computer-based melanoma diagnosis”, 22nd Fuzzy System Symposium, Sapporo, Sept. 6-8, 2006.
- 5. 4研究背景 本研究の目的 メラノーマ識別精度の向上 ? 母斑/メラノーマの区別 ?畳み込みニューラルネットワーク ?Fine-tuningによる転移学習 ?形状に着目した前処理 ?診断根拠の提示[2] 先行研究 ?セグメンテーション ?アンサンブル学習 ?半教師あり学習 手法の改良 [2] S.Murabayashi, H.Iyatomi, “Automated melanoma diagnosis and feasibility on presentation of its evidence with deep transfer learning” in The Institute of Electronics, Information and Communication Engineers, 2018.
- 7. 6先行研究 先行研究: 畳み込みニューラルネットワークによる学習 [3] 従来手法: ハンドメイドによる複雑な処理を伴う学習 特徴量抽出 識別入力 出力 ハンドメイド 機械学習 特徴量抽出 & 識別入力 出力 畳み込みニューラルネットワーク 特徴量さえも機械に抽出させることで識別精度が向上 [3] A.Krizhevsky, I.Sutskerver, GE.Hinton, “ImageNet Classification with Deep Convolutional Neural Networks”, In Proc. NIPS, 2012. 畳み込みニューラルネットワーク
- 8. 7先行研究 前半: 事前学習済みモデルのパラメータを利用 後半: ターゲット?タスクで再学習 [4] [4] R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2014. 重みの転移 固定 再学習 学習済みモデル Fine-tuning による転移学習
- 9. 8先行研究 母斑とメラノーマには形状の差異がある ?母斑: 一定の秩序を保ちゆっくり成長 ? 左右対称 ?メラノーマ: 無作為かつ盛んに成長 ? 左右非対称 前処理で、腫瘍領域の長軸位置合わせを行うことで 差異を顕著化し、特徴量抽出をサポート[5] [5] T.Yoshida, H.Iyatomi, “Alignment of Major Axis for Automated Melanoma Diagnosis with Deep Learning Approach” in 31st Fuzzy System Symposium, September 2015. 母斑 メラノーマ 形状に着目した前処理
- 11. データセット: 母斑: 3,777例 メラノーマ: 824例 （計4,601例） train : test = 4 : 1 モデル: VGG16（ImageNetで学習済み）をベース[6] 全結合層のみを再学習 学習パラメータ: 学習率: 0.001 学習回数: 70 バッチサイズ: 20 10実験 実験 目的 長軸位置合わせの有無による識別精度の変化を検証 [6] K.Simonyan, A.Zisserman, “VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION” Published as a conference paper at ICLR 2015
- 12. 実験①: ランダムクロップのみ 実験②: 長軸位置合わせ + ランダムクロップ 11実験 実験 原画像 256 x 256 リサイズ 224 x 224 ランダムクロップ 原画像 外接矩形による 長軸の決定 長軸を水平方向へ回転 256 x 256 リサイズ 224 x 224 ランダムクロップ
- 14. 実験①: 特異度: 85.8% 感度: 71.1% 識別精度: 83.2% 実験②: 特異度: 87.2% 感度: 78.3% 識別精度: 85.6% 13結果 識別結果 出力 母斑 メラノーマ 正 答 母斑 648 107 メラノーマ 48 118 特異度: 母斑を正しく識別した割合 感度: メラノーマを正しく識別した割合 長軸位置合わせにより 2.4%の識別精度を改善 出力 母斑 メラノーマ 正 答 母斑 658 97 メラノーマ 36 130
- 16. 考察と今後の課題 15 ?識別精度 皮膚科専門医[7]: 75?84% 識別器: 85.6% ? 長軸位置合わせはメラノーマ識別に有効 ?転移学習のベースモデルにメラノーマは含まれないが高い成果 ? 画像の汎用的な特徴を効率良く学習 ?学習においては適切なパラメータチューニングが必要 ?識別精度向上を目的とした画像処理やネットワークの改良 ? セグメンテーション/アンサンブル学習等の検討 ?ISIC2018コンペティションで使用された データセット「HAM10000」への挑戦 ? 体毛除去等に必要な知識の習得 考察と今後の課題 [7] Argenziano G, Soyer HP, Chimenti S et al: Dermoscopy of pigmented skin lesions: Results of a consensus meeting via the Internet. Journal of Academy of Dermatology, 2003