/Medical-Image-Segmentation-Loss

collection of loss functions in medical image segmentation

Medical-Image-Segmentation-Loss

Collection of loss functions in medical image segmentation

  • Using multi task learning as regularization
  • Dice loss and its variants
  • Cross-Entropy loss and its variants
  • Tversky loss and its variants
  • Adversarial loss
  • Auto-encoder loss
  • Micellaneous: Overlapping loss, Threshold map loss

2016

  1. V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation, Fausto Milletari et al, Arxiv. pdf

2017

  1. Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations, Carole H. Sudre et al, Arxiv. pdf

2018

  1. Densely Deep Supervised Networks with Threshold Loss for Cancer Detection in Automated Breast Ultrasound, Na Wang et al, MICCAI. pdf
  2. A Novel Focal Tversky Loss Function with Improved Attentation Unet for Lesion Segmentation, Nabila Abraham et al, Arxiv. pdf
  3. AnatomyNet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomy, Wentao Zhu et al, Arxiv. pdf
  4. 3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes, Ken C. L. Wong et al, Arxiv. pdf
  5. BESNet: Boundary-Enhanced Segmentation of Cellsin Histopathological Images, Hirohisa Oda et al, Arxiv. pdf
  6. Deep Multi-Task and Task specific Feature Learning Network for Roubust Shape Preserved Organ Segmentation, Chaowei Tan et al, ISBI. pdf

2019

  1. A Distance Map Regularized CNN for Cardiac Cine MR Image Segmentation, Shusil Dangi et al, Arxiv. pdf
  2. Psi-Net: Shape and boundary aware joint multi-task deep network for medical image segmentation, Balamurali Murugesan et al, Arxiv. pdf
  3. Bottleneck Supervised U-Net for Pixel-wise Liver and Tumor Segmentation, Song LI et al, Arxiv. pdf
  4. Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss, Guotai Wang et al, Arxiv. pdf