The document presents a framework named REMIC for multi-domain image completion, addressing challenges when certain image domains are missing. It integrates shared content encoding and domain-specific style encoding to complete missing information effectively across different datasets, demonstrating significant performance improvements. The framework is applicable to both medical and natural images and is evaluated on various datasets with detailed results compared to existing models.

1. Multi-Domain Image Completion
for Random Missing Input Data
Stanford University, NVIDIA, and National Institutes of Health
Yonsei University Severance Hospital CCIDS
Choi Dongmin

2. Introduction
• Multi-domain images could provide complementary knowledge
- ex. Four MRI modalities (T1, T1CE, T2, FLAIR) provide distinct features to locate tumor
boundaries from different diagnosis perspective
- ex. Person re-identification across different cameras or times
• However, some image domains might be missing in practice
- Solution 1. Nearest neighbor approach : lack of semantic consistency
- Solution 2. Generative models
• ReMIC (Representational disentanglement schemes for Multi-domain
Image Completion)
- -to- image completion framework
- utilized for the high-level task by joint training (ex. segmentation)
- completes the missing domains given random distributed numbers of visible domains
- consistent performance improvement on three datasets
n n

3. Related Works
Image-to-Image
Translation
J.Y Zhu et al. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. ICCV 2017
- Impressive performance via cycle-consistency loss
- only 1-to-1 mapping
• CycleGAN

4. Related Works
Image-to-Image
Translation
Y Choi et al. StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation. CVPR 2018
J Yoon et al. RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using GANs. ICML 2018
- Multi-domain image generation
- only 1-to- mapping (generation is always conditioned on the single input
image as the only source domain)
n
• StarGAN & RadialGAN
StarGAN RadialGAN

5. Related Works
Image-to-Image
Translation
D Lee et al. CollaGAN: Collaborative GAN for Missing Image Data Imputation. CVPR 2019
- Collaborative model to incorporate multiple domains for generating one
missing domain
- only -to-1 mappingn
• CollaGAN

6. Related Works
Image-to-Image
Translation
Red boxes are missing-domain images
Only ReMIC is -to- mappingn n

7. Related Works
Learning
Disentangled
Representations
• Learning Disentangled Representations
- to capture the full distribution of possible outputs by introducing a random style code
- to transfer information across domains for adaptation
- InfoGAN and -VAE learn the disentangled representation in unsupervised mannerβ
Xi Chen et al. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. NIPS 2016
I Higgins et al. beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework. ICLR 2017
https://www.slideshare.net/NaverEngineering/ss-96581209

8. Related Works
Learning
Disentangled
Representations
• DRIT and MUNIT
- disentangles content and attribute features in image translation
- However, only 1-to-1 translation
H.Y Lee et al. Diverse image-to- image translation via disentangled representations. ECCV 2018
X Huang et al. Multimodal Unsupervised Image-to-Image Translation. ECCV 2018

9. Related Works
Learning
Disentangled
Representations
• Liu et al
- tackles multi-domain learning cross-domain latent code
- less discussion about the domain-specific style code
Liu et al. A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation. NIPS 2018

10. Related Works Medical
Image Synthesis
• Previous works also discuss how to extract representations from multi-
modalities especially for segmentation with missing modalities
- However, fuse the features from multiple modalities but not from the perspective of
representation disentanglement
V Nguyen et al. Cross-domain synthesis of medical images using efficient location-sensitive deep network. MICCAI 2015
M Havaei et al. HeMIS: Hetero-Modal Image Segmentation. MICCAI 2016
A Chartsias et al.Multimodal mr synthesis via modality-invariant latent representation. IEEE transactions on medical imaging 2017

11. Method
Red boxes are
missing-domain images

12. Method
- Image decomposition
: Shared content structure (skeleton) + Unique characteristics (flesh)
- Missing image reconstruction during the testing
: Shared skeleton from available domains + Sampled flesh from the learned model
Style code (Domain-specific)
- Style encoder Es
i (xi) = si (1 ≤ i ≤ N)
Content code (Shared)
- Content encoder Ec
(x1, x2, …, xN) = c

13. Method
- Content codes visualization (randomly selected 8 out of 256 channels) of BraTs
: Various focuses on different anatomical structures (ex. tumor, brain, skull) are
demonstrated by different channel-wise feature maps
Input images

14. Method
- Generation : Style codes from a prior distribution + Content Code
-
si c
Gi(c, si) = ˜xi
Image Generation Process

15. Method
Segmentation Branch
- Segmentation generator after content codes
- Assumption : The content codes contain essential image structure information
- Joint training (generation loss + segmentation Dice loss)
: adaptively learn how to generate missing images
GS

16. Method
Training Loss
Image Consistency Loss
Latent Consistency Loss
Adversarial Loss
Reconstruction Loss
Segmentation Loss

17. Method
Total Loss
Training Loss
Adversarial
(λadv = 1)
Image
Consistency
(λx
cyc = 10)
Style Latent
Consistency
(λs
cyc = 1)
Reconstruction
(λrec = 20)
Content Latent
Consistency
(λc
cyc = 1)
Segmentation
(λseg = 1)

18. Experiments
• BraTS 2018 dataset
- Multi-modal brain MRI with four modalities : T1, T1Gd, T2, FLAIR
- Following CollaGAN, 218 training and 28 testing samples randomly selected
- A set of 2D slices (40,148 training / 5,340 test) extracted from 3D volumes
- Resized to 256 256
- Three tumor categories
: Enhancing tumor (ET), tumor core (TC), and whole tumor (WT)
×
D Lee et al. CollaGAN: Collaborative GAN for Missing Image Data Imputation. CVPR 2019
B.H Menze et al. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE TMI

19. Experiments
• ProstateX dataset
- Multi-parametric prostate MR scans for 98 subjects : T2, ADC, HighB
- 78 training and 20 testing samples randomly selected
- A set of 2D slices (3,540 training / 840 test) extracted from 3D volumes
- Resized to 256 256
- Prostate regions are manually labeled as the whole prostate (WP)
×
G Litjens et al. Computer-aided detection of prostate cancer in MRI. IEEE TMI

20. Experiments
• RaFD (Radboud Faces Database)
- Eight facial expressions
: neutral, angry, contemptuous, disgusted, fearful, happy, sand, and surprised
- Following StarGAN, adopt image from three camera angles with three gaze
directions
- 3,888 training (54 participants) / 936 test (13 participants)
- Cropped with the face in the center and Resized to 128 128×
Y Choi et al. StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation. CVPR 2018
http://www.socsci.ru.nl:8180/RaFD2/RaFD

21. Results
• Multi-Domain Image Completion on Domains
- Only One Missing Domain ( -to-1)
* Training : the one missing domain is randomly distributed
* Testing : Fix the one missing domain and generate outputs only on that
- More than One Missing Domains ( -to- )
* Training : randomly selected visible domains
* Testing : Fix while these domains are randomly selected visible
domains. Evaluate all the generated images
- Evaluation metrics
* NRMSE (Normalized Root Mean Squared Error)
* SSIM (Structural Similarity)
* PSNR (Peak Signal-to-Noise Ratio)
N
n
n n
k (k ∈ {1,…, N − 1})
k k
N

22. Results
• Multi-Domain Image Completion
- Comparison with MUNIT, StarGAN, and CollaGAN
- ReMIC w/o Recon : ReMIC without reconstruction loss (single missing domain)
- ReMIC-Random : random visible domains (multiple missing domains)(k = * ) k

23. Results
• Multi-Domain Image Completion
- Comparison with MUNIT, StarGAN, and CollaGAN
- ReMIC w/o Recon : ReMIC without reconstruction loss (single missing domain)
- ReMIC-Random : random visible domains (multiple missing domains)(k = * ) k

24. Results
• Multi-Domain Image Completion

25. Results
• Multi-Domain Image Completion

26. Results
• Multi-Domain Image Completion

27. Results
• Multi-Domain Image Completion

28. Results
• Multi-Domain Segmentation
- Oracle : fully supervised 2D U-Net variation without missing images
- Oracle+* : the missing images generated from the “*” method
with the pre-trained “Oracle” model (All : without any missing domains)
- ReMIC+Seg : separate content encoders for image generation and
segmentation tasks
- ReMIC+Joint : sharing the weights of content encoder for the two tasks

29. Conclusion
• A general framework for multi-domain image completion, given
that one or more input domains are missing
• Learning shared content and domain-specific style encoding
across multiple domains
• Well generalized to both natural and medical images
• Extended for a unified image generation and segmentation
framework for missing-domain segmentation task

30. Question
• According to this paper, “different modalities provide distinct
features to locate tumor boundaries from differential diagnosis
perspectives”.
But ReMIC uses a content code, which encodes the shared
skeleton, as an input for the connected segmentation generator.
Isn’t is a contradiction?

31. ICLR 2020 Reviews
• The main contribution is representational disentanglement,
namely the content and style separation, but there is no explicit
evidence that this separation is really happened
• Evaluation on high-resolution dataset such as CelebHQ and other
conventional metrics such as FID
https://openreview.net/forum?id=rkg_wREYDS

32. Appendix. A : Implementation Details
• A.1 Hyperparameters
- Adam optimizer
- Batch size 1 and 100,000 iterations
- Style code dimension : 8
- During testing, a fixed style code of 0.5 in each dimension
• A.2 Network Architectures (Check details in paper)
- ReMIC is developed on the backbone of MUNIT
- Unified Content Encoder : Down-sampling module + Residual Blocks (IN)
- Style Encoder : Down-sampling module + Residual Blocks + GAP + FC
- Generator : Four residual blocks + Up-sampling + AdaIN*
- Discriminator : Four convolutional blocks
- Segmentor : U-Net shaped network
(β1 = 0.5, β2 = 0.999)
X Huang et al. Arbitrary style transfer in real-time with adaptive instance normalization. ICCV 2017

33. Appendix. C : Extended Ablation Study and
Results for Multi-domain Segmentation
• C.4 Analysis of missing-domain segmentation results

34. Thank you