Improved Inference via Deep Input Transfer

Although numerous improvements have been made in the field of image segmentation using convolutional neural networks, the majority of these improvements rely on training with larger datasets, model architecture modifications, novel loss functions, and better optimizers. In this paper, we propose a new segmentation performance boosting paradigm that relies on optimally modifying the network's input instead of the network itself. In particular, we leverage the gradients of a trained segmentation network with respect to the input to transfer it to a space where the segmentation accuracy improves. We test the proposed method on three publicly available medical image segmentation datasets: the ISIC 2017 Skin Lesion Segmentation dataset, the Shenzhen Chest X-Ray dataset, and the CVC-ColonDB dataset, for which our method achieves improvements of 5.8%, 0.5%, and 4.8% in the average Dice scores, respectively.

[1]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[2]  Wei Zeng,et al.  Chest X-Ray Analysis of Tuberculosis by Deep Learning with Segmentation and Augmentation , 2018, 2018 IEEE 38th International Conference on Electronics and Nanotechnology (ELNANO).

[3]  Christopher Joseph Pal,et al.  Learning normalized inputs for iterative estimation in medical image segmentation , 2017, Medical Image Anal..

[4]  拓海 杉山,et al.  “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .

[5]  Amlan Chakrabarti,et al.  A Brief Survey of Recent Edge-Preserving Smoothing Algorithms on Digital Images , 2015, ArXiv.

[6]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Yuxin Cui,et al.  A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images , 2018, Medical & Biological Engineering & Computing.

[8]  Dr. Kailash Shaw,et al.  Skin Lesion Analysis towards Melanoma Detection , 2018 .

[9]  Alan L. Yuille,et al.  Adversarial Examples for Semantic Segmentation and Object Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[10]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Sylvain Paris,et al.  Automatic Portrait Segmentation for Image Stylization , 2016, Comput. Graph. Forum.

[12]  Ross B. Girshick,et al.  Mask R-CNN , 2017, 1703.06870.

[13]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[14]  Yoshua Bengio,et al.  The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[15]  Ghassan Hamarneh,et al.  Select, Attend, and Transfer: Light, Learnable Skip Connections , 2018, MLMI@MICCAI.

[16]  Matthew B. Blaschko,et al.  The Lovasz-Softmax Loss: A Tractable Surrogate for the Optimization of the Intersection-Over-Union Measure in Neural Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[17]  Noel C. F. Codella,et al.  Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC) , 2016, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[18]  George Papandreou,et al.  Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation , 2018, ECCV.

[19]  Tanveer F. Syeda-Mahmood,et al.  3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes , 2018, MICCAI.

[20]  Seyed-Ahmad Ahmadi,et al.  V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).

[21]  Ghassan Hamarneh,et al.  Topology Aware Fully Convolutional Networks for Histology Gland Segmentation , 2016, MICCAI.

[22]  Ghassan Hamarneh,et al.  Star Shape Prior in Fully Convolutional Networks for Skin Lesion Segmentation , 2018, MICCAI.

[23]  Ghassan Hamarneh,et al.  Combo Loss: Handling Input and Output Imbalance in Multi-Organ Segmentation , 2018, Comput. Medical Imaging Graph..

[24]  Vijaya B. Kolachalama,et al.  Segmentation of Glomeruli Within Trichrome Images Using Deep Learning , 2018, bioRxiv.

[25]  Samy Bengio,et al.  Adversarial examples in the physical world , 2016, ICLR.

[26]  Fernando Vilariño,et al.  Towards automatic polyp detection with a polyp appearance model , 2012, Pattern Recognit..