Reading List

Focus on machine learning (especially transfer learning) and its application on biomedical data processing and analysis.

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Part 1. Transfer learning ( Domain Adaptation ):

1.1 Survey

• Wang M , Deng W . Deep Visual Domain Adaptation: A Survey[J]. Neurocomputing, 2018. [paper]⭐⭐⭐⭐

1.2 Method

• Cavallari G B, Ribeiro L S F, Ponti M A. Unsupervised representation learning using convolutional and stacked auto-encoders: a domain and cross-domain feature space analysis[J]. arXiv preprint arXiv:1811.00473, 2018. [paper] ⭐⭐ ​
• Long M, Wang J, Ding G, et al. Transfer joint matching for unsupervised domain adaptation[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2014: 1410-1417. [paper] ⭐⭐⭐
• M. Long , H. Zhu , J. Wang , M.I. Jordan , Unsupervised domain adaptation with residual transfer networks, in: Proceedings of the Advances in Neural Information Processing Systems, 2016, pp. 136–144 . ⭐⭐⭐
• Cai G , Wang Y , Zhou M , et al. Unsupervised Domain Adaptation with Adversarial Residual Transform Networks[J]. 2018.[[paper]]:star::star::star:

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Part 2. Representation Learning on Graphs

2.1 Survey

2.2 Method

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Part 3. Machine Learning in Medical Image Analysis

3.1 Survey

• Yi X, Walia E, Babyn P. Generative adversarial network in medical imaging: A review[J]. arXiv preprint arXiv:1809.07294, 2018. [paper]

3.2 Segmentation

• Kao P Y, Ngo T, Zhang A, et al. Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction[J]. arXiv preprint arXiv:1807.07716, 2018. [paper] ⭐⭐⭐
• Ghafoorian M, Mehrtash A, Kapur T, et al. Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation[C]// International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2017:516-524.[paper] ⭐⭐
• Kamnitsas K, Baumgartner C, Ledig C, et al. Unsupervised domain adaptation in brain lesion segmentation with adversarial networks[C]//International Conference on Information Processing in Medical Imaging. Springer, Cham, 2017: 597-609. [paper] ⭐⭐⭐
• Ganaye P A, Sdika M, Benoit-Cattin H. Semi-supervised learning for segmentation under semantic constraint[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2018: 595-602. [paper] ⭐⭐⭐
• Falk, T., et al. (2018). "U-Net: deep learning for cell counting, detection, and morphometry." Nature Methods.:star::star:
• Dou Q, Ouyang C, Chen C, et al. Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial Loss[J]. arXiv preprint arXiv:1804.10916, 2018.[paper]⭐⭐⭐

3.3 Disease Diagnosis

• Lundberg S M, Nair B, Vavilala M S, et al. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery[J]. Nature Biomedical Engineering, 2018, 2(10): 749. [paper] ⭐⭐⭐⭐

3.4 Others

• Vakli P, Deák-Meszlényi R J, Hermann P, et al. Transfer learning improves resting-state functional connectivity pattern analysis using convolutional neural networks[J]. GigaScience, 2018. [paper] ⭐⭐
• Zhu B, Liu J Z, Cauley S F, et al. Image reconstruction by domain-transform manifold learning[J]. Nature, 2018, 555(7697): 487. [paper] ⭐⭐⭐⭐⭐
• Zhang K, Zuo W, Chen Y, et al. Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising[J]. IEEE Transactions on Image Processing, 2017, 26(7): 3142-3155. [paper]⭐⭐⭐⭐
• Jiang D, Dou W, Vosters L, et al. Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network[J]. Japanese journal of radiology, 2018, 36(9): 566-574.[paper] ⭐⭐⭐ ​
• Chen H, Zhang Y, Kalra M K, et al. Low-dose CT with a residual encoder-decoder convolutional neural network[J]. IEEE transactions on medical imaging, 2017, 36(12): 2524-2535.[paper] ⭐⭐⭐

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Part 4. Others

4.1 explanatory

• Ribeiro M T, Singh S, Guestrin C. Why should i trust you?: Explaining the predictions of any classifier[C]//Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, 2016: 1135-1144. [paper] ⭐⭐⭐
• Lundberg S M, Lee S I. A unified approach to interpreting model predictions[C]//Advances in Neural Information Processing Systems. 2017: 4765-4774. [paper] ⭐⭐⭐⭐

4.2 Loss Function

• [restoration]Zhao H, Gallo O, Frosio I, et al. Loss functions for image restoration with neural networks[J]. IEEE Transactions on Computational Imaging, 2017, 3(1): 47-57. [paper]⭐⭐⭐

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