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[NIPS 2020] The code release of paper 'Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization'

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lddg's Introduction

Domain Generalization for Medical Imaging Classification with Linear Dependency Regularization

The code release of paper 'Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization' NeurIPS 2020. The pre-print paper can be found in Arxiv.

How to use

First, you need to install the package surface-distance https://github.com/deepmind/surface-distance and SimpleITK

pip install SimpleITK

Then run the following command to train and evaluate the performance of the model

python3 train_lddg.py -t i

where i means set_i is the target domain.

Segmentation Reuslts

Please cite our paper if you find it's useful.

@article{li2020domain, title={Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization}, author={Li, Haoliang and Wang, YuFei and Wan, Renjie and Wang, Shiqi and Li, Tie-Qiang and Kot, Alex C}, journal={arXiv preprint arXiv:2009.12829}, year={2020} }

lddg's People

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jiananim dishamohini 3bao david-az 601sung cv-ip ncolab chunfenglian tonyckc

lddg's Issues

Code problem

LDDG/train_lddg.py

Line 164 in 0242b2b

U, S_spinal, V = torch.svd(feature[0:2000])

why only use fisrt 2000 features, not all ?
just to reduce compuation ?

Paper problem

I have a question about your paper,The last third line on Page 3:‘’in other words,the rank of matirx is expected to be 1"?

The code for skin lesion classification

Hi, I find this repo only has the code for spinal cord gray matter segmentation. Is there any plan to release the code for skin lesion classification in the future?

Spinal Cord Challenge Dataset

Hello! Your work is great and impressive, but now I encounter a problem with the downloading of Spinal Cord Challenge Dataset. Could you please tell me how to get this dataset? Thanks very much.

About data downloading

Hi! Could you please tell me how to download the MSK, UDA, and SONIC datasets?

first of all, I would like to thank you for your great work. I have some doubts about your code, can you please reply to me with the following request: why did you set the latent_dim= 8 ?, why did you take the svd from the first 2000 elements of the features matrix and why did you choose the low_rank_loss_spinal=S_spinal[2]?

Proof for Theorem 2

Hi, thank you for your great paper. I'm sorry that I can't understand parts of the proof for theorem 2.

(1) Here you assume L() denotes the cross-entropy loss with softmax operation. Does that mean y is the input of the softmax function? Because only in this case the linear relation between the target y and the source y's holds.

(2) I'm also confused how you derive from the first line to the second line (including how you take the sum over j outside of the loss function and how you change p* to pj).

(3) What's more, you said you used the upper bound of log-sum-exp function to derive the second line. And {a1, a2, ..., an} are the outputs of the softmax function. Then I'm not sure where the exponential comes in the log-sum-exp function that takes the exponential of a.

Thank you for your kind reply.

Classification

Is there any plan to release the code for classification task?

Model architecture for classification

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