Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Generate Abnor Echo Image
Search
Tsung-Yung Lu
September 28, 2018
Research
0
64
Generate Abnor Echo Image
2018SMIRS內容
Tsung-Yung Lu
September 28, 2018
Tweet
Share
More Decks by Tsung-Yung Lu
See All by Tsung-Yung Lu
DICOM RT Dose
higumalu
0
15
The GEMPix detector
higumalu
0
28
Respiratory Gating for Radiotherapy
higumalu
0
530
Cholescintigraphy
higumalu
0
120
DL in MRI
higumalu
0
88
Cardiac CT
higumalu
0
210
Class Report of PETCT Model
higumalu
0
48
淺談影像處理
higumalu
0
92
Tc99m
higumalu
0
130
Other Decks in Research
See All in Research
AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data
satai
1
220
Vision and LanguageからのEmbodied AIとAI for Science
yushiku
PRO
1
530
論文読み会 SNLP2025 Learning Dynamics of LLM Finetuning. In: ICLR 2025
s_mizuki_nlp
0
210
AWSで実現した大規模日本語VLM学習用データセット "MOMIJI" 構築パイプライン/buiding-momiji
studio_graph
2
520
数理最適化と機械学習の融合
mickey_kubo
16
9.3k
心理言語学の視点から再考する言語モデルの学習過程
chemical_tree
2
590
RHO-1: Not All Tokens Are What You Need
sansan_randd
1
170
SSII2025 [SS2] 横浜DeNAベイスターズの躍進を支えたAIプロダクト
ssii
PRO
7
4k
Google Agent Development Kit (ADK) 入門 🚀
mickey_kubo
2
1.8k
多言語カスタマーインタビューの“壁”を越える~PMと生成AIの共創~ 株式会社ジグザグ 松野 亘
watarumatsuno
0
120
Large Language Model Agent: A Survey on Methodology, Applications and Challenges
shunk031
16
9.9k
[輪講] SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
nk35jk
2
1k
Featured
See All Featured
Into the Great Unknown - MozCon
thekraken
40
2k
10 Git Anti Patterns You Should be Aware of
lemiorhan
PRO
656
61k
Understanding Cognitive Biases in Performance Measurement
bluesmoon
29
1.9k
What's in a price? How to price your products and services
michaelherold
246
12k
Building Better People: How to give real-time feedback that sticks.
wjessup
368
19k
Templates, Plugins, & Blocks: Oh My! Creating the theme that thinks of everything
marktimemedia
31
2.5k
"I'm Feeling Lucky" - Building Great Search Experiences for Today's Users (#IAC19)
danielanewman
229
22k
Measuring & Analyzing Core Web Vitals
bluesmoon
9
580
CoffeeScript is Beautiful & I Never Want to Write Plain JavaScript Again
sstephenson
162
15k
Refactoring Trust on Your Teams (GOTO; Chicago 2020)
rmw
35
3.1k
YesSQL, Process and Tooling at Scale
rocio
173
14k
Distributed Sagas: A Protocol for Coordinating Microservices
caitiem20
333
22k
Transcript
生成非正常腎臟超音波影像 提升分類準確性之研究 假體實驗 報告者:義守大學 醫學影像暨放射科學系 盧宗詠 2018/09/29
2 <over view> ➢ 研究動機 < motive> ➢ 目的 <purpose>
➢ 材料與方法 < method> ➢ 結果 <result> ➢ 結論與討論 <conclusion>
3 <研究動機>
4
5 “NORMAL”
6 <目的>
7 <purpose> ➢ 解決影像資料集過少的問題 ➢ 比較人為產生的非正常影像 與實際非正常影像 經由神經網路訓練後的優缺分析 ➢ 探討人為加工的醫學影像是否有助於診斷輔
助系統的開發
8 <材料與方法>
9 < materials > Sonosite 180+ 手提式超音波儀 C60
(5-2MHz) Transducer US-1B ABDFAN腹部超音波假體 造影角度不限,深度為12與15公分之 右腎影像,總計蒐集1400張 (合成影像總計生成1600張)
10 < method> 將影像選取ROI並且正規化 從原始影像資料集隨機取出部分影像並透 過自行編寫的matlab程式加工成非正常影像 建立四組資料集以便於進行訓練與驗證
透過卷積神經網絡建立三種判斷模型 CNN模型具有三個2D卷積層(濾波數分別為32、64、128個, 卷積核大小5*5、3*3、3*3)、三個激活層(其激活函數皆使 用ReLU、三個池化層(池化窗口大小皆為3,3) 藉由驗證資料集比較三種模型的差異 繪製分析圖以方便觀察結果
11
12 < method_01.bmp> 1400
13 < method_02.bmp > 1400 NORMAL X1000 ABNORMAL X400
14 < method_03.bmp> NORMAL X1000 X1600
15 < method_04_train_data.bmp> 1.正常 +非正常 (1100) 2.正常 +合成影像 (2300) 3.正常
+(非正常+合成) (2600) NORMAL X800 ABNORMAL X300 NORMAL X800 ABNORMAL X1500 NORMAL X800 ABNORMAL X1800
16 < method_05_validation_data.bmp> NORMAL 200 ABNORMAL (100+100) X400 validation
17 < method_06_train_CNN.py>
18 < method _07_predict_CNN.py > softmax() “NORMAL” “ABNORMAL”
19 < method_08 _ROC.py > ➢ TP (true positive):分類為非正常,實際上為非正常 ➢
TN (true negative):分類為正常實際上為正常 ➢ FP (false positive):分類為非正常,實際上為正常 ➢ FN (false negative):分類為正常,實際上為非正常
20 < method_09_ROC.py > ➢ 準確度,accuracy: 分類正確的比率 ➢ 敏感度, sensitivity:
非正常被分類成非正常的比率(有病判有病) ➢ 錯誤命中率: 正常被分類成正常的比率(沒病判沒病) ➢ 特異度, specificity : 正常被分類為非正常的比率(沒病判有病) ➢ 陽性預測值 :被分類為非正常,實際上為非正常的比率 ➢ F-measure(F度量,F1) :一種同時兼顧查準率(precision)與查全率 (recall)的度量方式,應用於資訊檢索(information retrieval)領域 的成效評估
21 < method_10_ROC.py> ➢ TPR(敏感度, sensitivity) = TP / P
➢ FPR(錯誤命中率) = FP / N ➢ F-measure(F度量,F1) = (2 x TPR x PPV) / (TPR + PPV)
22 <結果>
23 < result> ➢ F-measure ➢ ROC curve with AUC
(receiver operating characteristic curve)
24 < result02.bmp> nor+abnor nor+syn nor+(abnor+syn) True Positive 116 168
198 True Negative 198 157 158 False Positive 2 43 42 False Negative 84 32 2 accuracy 0.785 0.8125 0.89 sensitivity 0.58 0.84 0.99 specificity 0.99 0.785 0.79 false alarm rate 0.01 0.215 0.21 F-measure 0.7295 0.8175 0.9
25 < result01.bmp> nor+abnor nor+syn nor +(abnor+syn) TPR 0.58 0.84
0.99 FPR 0.01 0.215 0.21
26 < result03_ROC.bmp>
27 <結論與討論>
28 < conclusion > ➢ 生成影像能夠有效的解決非正常影像資料 量不足的問題 ➢ 生成影像作為訓練資料可以提升分類模型 的準確性
29 < discussion > ➢ 使用更為複雜的神經網路或者不同的影像生成 方式是否能使準確度提升? ➢ 若生成影像由專業的臨床醫師設計是否能使準 確度更高?
➢ 是否有更好的辦法能夠有效的提升預測模型的 準確度?
THANKS FOR LISTENING github.com/higumalu
[email protected]
Q & A