Visual Analytics from Feature Design to Deep Neural Networks Understanding

Nicola Pezzotti and Anna Vilanova

Traditional Machine Learning

Feature SpaceHigh-Dimensional

Feature VectorsData

Feature

Extraction

It’s a dog

It’s a cat

Classifier (supervised)

Clustering(unsupervised)

…

Ite

ms(e

.g.,p

ictu

res)

Features (e.g., pixels)

Feature Space Design

Feature SpaceHigh-Dimensional

Feature VectorsData

Feature

Extraction

Ite

ms(e

.g.,p

ictu

res)

Features (e.g., pixels)

• Based on humane knowledge

and reasoning, assumptions

• Automatic Feature Extraction

Training/Learning

Feature

Space

Data

It’s a dog

It’s a cat

Classifier (supervised)

dogs

cats

Black Box

Use of Machine Learning – As a Black Box

Feature

Space

It’s a cat

Classifier (supervised)

It’s a dog

Black Box

Feature

Space

It’s a dog

Classifier (supervised)

It’s a cat

Use of Machine Learning – As a Black Box

Black Box

Feature

Space

It’s a cat

Classifier (supervised)

It’s a dog

Use of Machine Learning – As a Black Box

Black Box

Feature

Space

It’s a cat

Classifier (supervised)

It’s a dog

Use of Machine Learning – As a Black Box

Black Box

Black Box difficult acceptance

Feature

SpaceIt’s benign

Classifier (supervised)

It’s malign

Black Box

Feature

Space

Data

It’s a dog

It’s a cat

Classifier (supervised)

dogs

cats

User in the loop

Visual Analytics

Visualization

InteractionData Analysis

Machine learning

Visual Analytics – Opening the black-box

● Not enough getting a good or bad result you want to reason about the

process and the methods

● Why do I get a good or bad result?

Relation to feature space.

What makes a dog a dog ? what makes a cat a cat?

Important in medical applications:

What features makes a benign tumor benign or malign?

● Training Data

● Evaluate Design Decisions

Feature Space Design

Feature SpaceHigh-Dimensional

Feature VectorsData

Feature

Extraction

Ite

ms(e

.g.,p

ictu

res)

Features (e.g., pixels)

• Based on humane knowledge

and reasoning, assumptions

• Automatic Feature Extraction

Add reference

Why automation is not enough?

Numbers do not tell the whole story

http://blog.revolutionanalytics.com/2017/05/the-datasaurus-dozen.html

Feature Space Visualization/UnderstandingParallel Coordinates

SPLOM

High-Dimensional

Feature Vectors

Ite

ms(e

.g.,p

ictu

res)

Features (e.g., pixels)

Dimensionality Reduction

Dimensionality

Reduction

Embedding

Dim-1

Dim

-2

Assumption: redundancy in the data

Goal: preserve “structure” of the HD space

High-Dimensional

Feature Vectors

Ite

ms(e

.g.,p

ictu

res)

Features (e.g., pixels)

What does “structure” mean?

Dimensionality Reduction (DR)

18

● Build a lower dimension in which distances between points reflect similarities in the

HD data

● Minimize an objective function that measure the discrepancy between similarities in

the data and similarities in the map

Dimensionality Reduction (DR)

19

● Build a lower dimension in which distances between points reflect similarities in the

HD data

● Minimize an objective function that measure the discrepancy between similarities in

the data and similarities in the map

Types of Dimensionality Reduction

Transformation to a projection/subspace (for Vis mainly 2D):

○ Linear:Resulting attributes are linear combination of existing attributes

○ Principal Component Analysis (PCA)

○ Linear Discriminant Analysis (LDA)

○ …

○ Non Linear: Resulting attributes do not have straightforward relation to original attributes

■ Multi-Dimensional Scaling (MDS) - preserve distances

■ t-Distributed Stochastic Neighbor Embedding (t-SNE) – preserve neighbourhods

https://lvdmaaten.github.io/tsne/

■ …

MNIST dataset – Handwritten numbers

21

1

0

PCA on MNIST dataset

tSNE on MNIST dataset

2

3

4

56

7

8

1

0

9

PCA vs tSNE on MNIST dataset

PCA tSNE

2

3

4

56

7

8

1

0

9

Overview of systems that do feature analysis

201214.1 M Cancer Cases 8.2 M Cancer Deaths [WHO Report, 2014]

Prostate Cancer1 out of 6 men

Example of Visual Analytics using tSNE

Dose A

Dose C

Dose B

3Renata Raidou

Dose A

Dose C

Dose B

Treatment Conventional vs. tailored to tumor characteristics

R. Raidou et al. EuroVis 2015

Tumor Tissue Characterization

Gleason Scores (GS)

Appearant Diffusion Coefficient (MRI)

Dynamic Contrast Enhanced (MRI)

(Pharmacokinetic modelling)

…

R. Raidou et al. EuroVis 2015

Tumor Tissue Characterization: Current Visual Analysis

Feng et al. ECR2015

R. Raidou et al. EuroVis 2015

Visual Tool

R. Raidou et al. EuroVis 2015

Identification and Exploration of Intra-tumor Regions

R. Raidou et al. EuroVis 2015

Visual Analysis Tool

R. Raidou et al. EuroVis 2015

Employing Visual Analytics to Aid the Design of White Matter Hyperintensity Classifiers

R.G. Raidou, H. Kuijf, N. Sepasian, N. Pezzotti,

(clinician from UMC), M. Breeuwer, A. Vilanova

Deep Learning

Neuronal NetworkData

It’s a dog

It’s a cat

MOTIVATION

Res-Net and VGG

DenseNet

U-Net

Generative Semantic

Interpretable AI

Generative Semantic

Interpretable AI

From Training to Input Generation

GENERATIVE

Gradient is back-propagated&

parameters are adjusted

Forward Pass

It’s a Cat!It’s a Dog!

From Training to Input Generation

GENERATIVE

Gradient is back-propagated&

the Input is adjusted

Forward Pass

Show me

a Dog!

Show me

a Dog!

Feature Visualization

GENERATIVE

[Feature Visualization - Olah, Mordvinstev, Schubert - 2017]

GoogLeNet Architecture

GENERATIVE

[Feature Visualization - Olah, Mordvinstev, Schubert - 2017]

Conv2d0 mixed3a mixed4a mixed4b mixed4e

Problems of Generative Methods

● Difficult to interpret

● Generated through an optimization

● Scalability issuehow many filters can we analyze at the same time?

GENERATIVE

Badly trained network?How informative?

Interpretable AI

Generative Semantic

● Response of the Model against ALL the input○ Training set

○ Test set

○ Online examples

● Show the semantic relationships learned by the

network

● Scalability of computations is key○ Huge training sets

○ Cognitive overload of users

Visualization of Semantic Relationships

SEMANTIC

Latent Space Sampling

SEMANTIC Forward Pass

Trained Network

20739

[20, 7, 3, 9]

Latent Space Sampling

SEMANTIC Forward Pass

Trained Network

571117

[20, 7, 3, 9] [5, 7, 11, 17]

Good

Model

Bad

Model

SEMANTIC

Good

Model?

SEMANTIC

Hierarchical-SNE for Deep Network Analysis

92 s

ImageNet Test Set (100k)

SEMANTIC

[Hierarchical Stochastic Neighbor Embedding - Pezzotti et al. - 2016]

SEMANTIC

[Hierarchical Stochastic Neighbor Embedding - Pezzotti et al. - 2016]

SEMANTIC

[Hierarchical Stochastic Neighbor Embedding - Pezzotti et al. - 2016]

DeepEyes

SEMANTIC

● A semantic tool to support the design of Deep Neural Networks

Deep Neural Network

TrainingDesign

Trial & Error

DeepEyes

SEMANTIC

● A semantic tool to support the design of Deep Neural Networks

Deep Neural Network

TrainingDesign

DeepEyes

Informed Decisions

SEMANTIC

DeepEyes

[DeepEyes: Progressive Visual Analytics for Designing Deep Neural Networks - Pezzotti et al. - 2018]

SEMANTIC

DeepEyes

[DeepEyes: Progressive Visual Analytics for Designing Deep Neural Networks - Pezzotti et al. - 2018]

SEMANTIC

DeepEyes

[DeepEyes: Progressive Visual Analytics for Designing Deep Neural Networks - Pezzotti et al. - 2018]

SEMANTIC

DeepEyes: Detailed Analysis

[DeepEyes: Progressive Visual Analytics for Designing Deep Neural Networks - Pezzotti et al. - 2018]

Activation HeatmapInput Map Filter Map

SEMANTIC

DeepEyes: Detailed Analysis

[DeepEyes: Progressive Visual Analytics for Designing Deep Neural Networks - Pezzotti et al. - 2018]

Activation HeatmapInput Map Filter Map

Dead Filters

Conclusions

● Interpretability is important for ML models○ Validation

○ Improvement

● Scalability of analytics solutions is key○ Large datasets

○ Limited human cognitive power

● Interpretability will be even more relevant○ A shift towards unsupervised techniques

○ Active Learning solutions

○ Reinforcement Learning with Humans-in-the-loop

Conclusions

CONCLUSIONS

Thank you!Questions?