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Asymmetric Similarity Loss Function to Balance Precision and Recall in Highly Unbalanced Deep Medical Image Segmentation
Seyed Raein Hashemi,Seyed Sadegh Mohseni Salehi,Deniz Erdogmus,Sanjay P. Prabhu,Simon K. Warfield,Ali Gholipour +5 more
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A patch-wise 3D densely connected network with an asymmetric loss function, where large overlapping image patches for intrinsic and extrinsic data augmentation, a patch selection algorithm, and a patch prediction fusion strategy based on B-spline weighted soft voting to take into account the uncertainty of prediction in patch borders are developed.Abstract:
Fully convolutional deep neural networks have been asserted to be fast and precise frameworks with great potential in image segmentation. One of the major challenges in utilizing such networks raises when data is unbalanced, which is common in many medical imaging applications such as lesion segmentation where lesion class voxels are often much lower in numbers than non-lesion voxels. A trained network with unbalanced data may make predictions with high precision and low recall, being severely biased towards the non-lesion class which is particularly undesired in medical applications where false negatives are actually more important than false positives. Various methods have been proposed to address this problem including two step training, sample re-weighting, balanced sampling, and similarity loss functions. In this paper we developed a patch-wise 3D densely connected network with an asymmetric loss function, where we used large overlapping image patches for intrinsic and extrinsic data augmentation, a patch selection algorithm, and a patch prediction fusion strategy based on B-spline weighted soft voting to take into account the uncertainty of prediction in patch borders. We applied this method to lesion segmentation based on the MSSEG 2016 and ISBI 2015 challenges, where we achieved average Dice similarity coefficient of 69.8% and 65.74%, respectively, using our proposed patch-wise 3D densely connected network. Significant improvement in $F_1$ and $F_2$ scores and the area under the precision-recall curve was achieved in test using the asymmetric similarity loss layer and our 3D patch prediction fusion method. The asymmetric similarity loss function based on $F_\beta$ scores generalizes the Dice similarity coefficient and can be effectively used with the patch-wise strategy developed here to train fully convolutional deep neural networks for highly unbalanced image segmentation.read more
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Journal ArticleDOI
Combo loss: Handling input and output imbalance in multi-organ segmentation
Saeid Asgari Taghanaki,Saeid Asgari Taghanaki,Yefeng Zheng,S. Kevin Zhou,Bogdan Georgescu,Puneet Sharma,Daguang Xu,Dorin Comaniciu,Ghassan Hamarneh +8 more
TL;DR: In this article, a curriculum learning based loss function is proposed to handle the imbalance problem in both the input and output of a learning model for multi-organ segmentation, where the Dice similarity coefficient is leveraged to deter model parameters from being held at bad local minima and at the same time gradually learn better model parameters by penalizing for false positives/negatives using a cross entropy term.
PatentDOI
3D segmentation with exponential logarithmic loss for highly unbalanced object sizes
TL;DR: In this paper, a 3D segmentation with exponential logarithmic loss for highly unbalanced object sizes is provided, where an artificial neural network is trained to label an anatomical feature in medical images.
Journal ArticleDOI
Fully automatic brain tumor segmentation with deep learning-based selective attention using overlapping patches and multi-class weighted cross-entropy.
TL;DR: The proposed CNNs model is inspired by the Occipito-Temporal pathway which has a special function called selective attention that uses different receptive field sizes in successive layers to figure out the crucial objects in a scene to maximize the extraction of relevant features from MRI images.
Proceedings Article
Combo Loss: Handling Input and Output Imbalance in Multi-Organ Segmentation
Saeid Asgari Taghanaki,Yefeng Zheng,S. Kevin Zhou,Bogdan Georgescu,Puneet Sharma,Daguang Xu,Dorin Comaniciu,Ghassan Hamarneh +7 more
TL;DR: A new curriculum learning based loss function that leverages Dice similarity coefficient to deter model parameters from being held at bad local minima and at the same time gradually learn better model parameters by penalizing for false positives/negatives using a cross entropy term is introduced.
Posted Content
Lung Cancer Tumor Region Segmentation Using Recurrent 3D-DenseUNet
TL;DR: A data driven approach for lung tumor segmentation from CT scans is presented by using Recurrent 3D-DenseUNet, a novel fusion of Convolutional and Recurrent neural network, which shows more promise than crude intensity thresholding.
References
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Posted Content
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
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