Spike Detection (1D)

Spike detection - Simple Threshold• Null: No preprocessing, just apply a positive threshold to raw data• Negation: Signal is inverted before threshold. This is same as applying a negative threshold• Absolute Value: Same as applying both positive and negative thresholds simultaneously

Spike detection – Adaptive Threshold

• Ex. set an automatic amplitude threshold

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Spike detection – Energy Based

• NEO: Non-linear Energy Operator = − + − ,ℎ 1 ≤ ≤ 4• SNEO: Smoothed NEO

• Convolution of NEO with a smoothing window function

Spike detection – Matched Filter (MF)

• MF: raw signal convolved with a template• Template is an average of action potentials• Could be NEO + MF, Absolute Value + MF, etc

Spike detection – Wavelet

• Two different wavelet-based approaches presented in this work: (a) Wavelet multiresolution analysis is composed by wavelet decomposition and reconstruction through low-pass (LP) and high-pass (HP) filter banks; (b) How to map the wavelet scalogram

• SWT = Stationary Wavelet Transform

DL Object Detection (2D)

CNN Applications• Classification• Detection• Segmentation• …

Classification

Regional Convolutional Neural Networks (R-CNN)

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Girshick et al., Rich feature hierarchies for accurate object detectionand semantic segmentation, CVPR, 2014

① R-CNN② Fast R-CNN③ Faster R-CNN④ Mask R-CNN

Object Detection and Classification

R-CNN의 기본 프로세스

• Selective search 알고리즘을 사용하여 2000개의 region proposal을 추출• Selective Search: Selective Search for Object Recognition, J. R. R.

Uijings et al, IJCV13.• 모든 regional proposal 검출 영상을 같은 image size로 변환• CNN을 통하여 특징 추출 (forward propagation)• SVM (Support Vector Machine)을 통하여 추출한 특징을 분류하여 object classification• Per regional proposal image, CNN runs. Inference speed is slow. Also multiple stages of region proposals, CNN feature extraction, and classification via SVM.

You Only Look Once (YOLO)

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- Real-time Object Detection- Also Classification

① YOLO v1.0② YOLO v2.0③ YOLO v3.0

Deep Learning YOLO

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- YOLO = You Only Look Once- Real-time Object Detection- Joseph Redmon, Ph.D candidate,

University of Washington, CSE

YOLO 특징

• R-CNN은 detection을 위하여, regional proposal을 추출하고, classifier 사용• YOLO는 object bounding boxes와 class probabilities를 regression 방법으로 예측• 전체 구조가 single neural network frame• 장점

• Extremely fast: detection을 regression 방식으로 해결• YOLO sees whole image, not regions• YOLO can be general, working on all kinds of images

• 단점• Fast at detection, little weak at finding position of small objects

How YOLO Works1. Divide an input image into SxS grid cells2. grid cells들은 B개의 bounding box와, 각 bounding box에 대한 confidence score를 계산

Confidence Score = 3. 각 grid cell은 C개의 conditional class probability를 가짐.

Conditional Class Prob. = Pr(Classi | Object) 4. 각 bounding box는 x, y, w, h, confidence로 구성

(x,y): bounding box의 중심점(w,h): width & height

5. 각 grid cell은 (B*5+C) vector를 가짐. B*5: B개의 bounding box + x, y, w, h, confidence 즉 5. C는 class의 수

6. Each whole image has (S*S*(B*5+C)) tensor. 정보 기록7. At test, compute class-specific confidence score

Class Specific Confidence Score =

What is IOU?

Medical Computer Aided Diagnosis (CAD) System

Deep Learning Model: Detection & Classification:

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YOLO-based Medical CAD

YOLO-based Medical CAD System• Detection Classification

Deep Learning Segmentation

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Full Resolution SegNet

Deep Learning Models: Detection, Segmentation, & Classification

34T.-S. Kim et al., A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification, Int. J. of Medical Informatics, 2018

Deep Learning Models: Detection, Segmentation, & Classification

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