Medical image matching using variable randomized undersampling probability pattern in data acquisition

Jang, Jong Whan; Eo, Tae Joon; Kim, Min-Oh; Choi, Narae; Dong-Yup, Han; Kim, Yejin; Hwang, Dosik

doi:10.1109/elinfocom.2014.6914453

Cited by 9 publications

(2 citation statements)

References 3 publications

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“…Data-level approaches mainly alleviate the class imbalance by undersampling the majority classes [9] and oversampling the minority classes [10]. However, the majority undersampling limits the information of available data for training and the minority oversampling can lead to overfitting.…”

Section: Introductionmentioning

confidence: 99%

Loss Weightings for Improving Imbalanced Brain Structure Segmentation Using Fully Convolutional Networks

et al. 2021

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Brain structure segmentation on magnetic resonance (MR) images is important for various clinical applications. It has been automatically performed by using fully convolutional networks. However, it suffers from the class imbalance problem. To address this problem, we investigated how loss weighting strategies work for brain structure segmentation tasks with different class imbalance situations on MR images. In this study, we adopted segmentation tasks of the cerebrum, cerebellum, brainstem, and blood vessels from MR cisternography and angiography images as the target segmentation tasks. We used a U-net architecture with cross-entropy and Dice loss functions as a baseline and evaluated the effect of the following loss weighting strategies: inverse frequency weighting, median inverse frequency weighting, focal weighting, distance map-based weighting, and distance penalty term-based weighting. In the experiments, the Dice loss function with focal weighting showed the best performance and had a high average Dice score of 92.8% in the binary-class segmentation tasks, while the cross-entropy loss functions with distance map-based weighting achieved the Dice score of up to 93.1% in the multi-class segmentation tasks. The results suggested that the distance map-based and the focal weightings could boost the performance of cross-entropy and Dice loss functions in class imbalanced segmentation tasks, respectively.

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Section: Introductionmentioning

confidence: 99%

Loss Weightings for Improving Imbalanced Brain Structure Segmentation Using Fully Convolutional Networks

et al. 2021

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“…At the data-level, the objective is to balance the class distribution through re-sampling the data space [29] including SMOTE (Synthetic Minority Over-sampling Technique) of the positive class [12,31] or under-sampling of the negative class [25]. However, these approaches often lead to remove some important samples or add redundant samples to the training set.…”

Section: Introductionmentioning

confidence: 99%

Conditional Generative Adversarial Refinement Networks for Unbalanced Medical Image Semantic Segmentation

Rezaei

Yang

Harmuth

et al. 2019

2019 IEEE Winter Conference on Applications of Computer Vision (WACV)

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We propose a new generative adversarial architecture to mitigate imbalance data problem in medical image semantic segmentation where the majority of pixels are belong to healthy region and few belong to lesion or non-health region. A model trained with imbalanced data tends to bias toward healthy data which is not desired in clinical applications and predicted outputs by these networks have high precision and low sensitivity. We propose a new conditional generative refinement network with three components: a generative, a discriminative, and a refinement network to mitigate unbalanced data problem through ensemble learning. The generative network learns to segment at the pixel level by getting feedback from the discriminative network according to the true positive and true negative maps. On the other hand, the refinement network learns to predict the false positive and the false negative masks produced by the generative network that has significant value especially in medical application. The final semantic segmentation masks are then composed by the output of the three networks. The proposed architecture shows state-of-theart results on LiTS-2017 for liver lesion segmentation, and two microscopic cell segmentation datasets MDA231, PhC-HeLa. We have achieved competitive results on BraTS-2017 for brain tumour segmentation.

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Handling Unbalanced Data in Clinical Images

Verma

2022

Advanced Healthcare Systems

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Medical image matching using variable randomized undersampling probability pattern in data acquisition

Cited by 9 publications

References 3 publications

Loss Weightings for Improving Imbalanced Brain Structure Segmentation Using Fully Convolutional Networks

Loss Weightings for Improving Imbalanced Brain Structure Segmentation Using Fully Convolutional Networks

Conditional Generative Adversarial Refinement Networks for Unbalanced Medical Image Semantic Segmentation

Handling Unbalanced Data in Clinical Images

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