Ordinal Regression with Neuron Stick-Breaking for Medical Diagnosis

Liu, Xiaofeng; Zou, Yang; Song, Yuhang; Yang, Chao; You, Jane; Kumar, B. V. K. Vijaya

doi:10.1007/978-3-030-11024-6_23

Cited by 44 publications

(44 citation statements)

References 19 publications

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“…Specifically, the MOW-Net significantly improves the recall of the unsure class by 0.28 over the previous best result. This is significant for the clinical diagnosis since a higher recall of the unsure class can [11] 0.542 0.548 0.794 0.648 0.568 0.624 0.594 0.489 0.220 0.303 NSB [10] 0.553 0.565 0.641 0.601 0.566 0.594 0.580 0.527 0.435 0.476 UDM [9] 0.548 0.541 0.767 0.635 0.712 0.515 0.598 0.474 0.320 0.382 CORF [12] 0. encourage more follow-ups and reduce the probabilities of the nodules that are misdiagnosed as malignant or benign. In addition, the precision of benign and the recall of the malignant get a great improvement.…”

Section: Classification Performancementioning

confidence: 99%

“…However, the UDM has some additional parameters that need to be carefully tuned. The neural stickbreaking (NSB) method calculates the probabilities through the C − 1 predicted classification bounds, where C is the number of classes [10]. The unimodal method makes each fully-connected output follows a unimodal distribution such as Poisson or Binomial [11].…”

Section: Introductionmentioning

confidence: 99%

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Meta Ordinal Weighting Net For Improving Lung Nodule Classification

Lei

Shan

Zhang

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The progression of lung cancer implies the intrinsic ordinal relationship of lung nodules at different stages-from benign to unsure then to malignant. This problem can be solved by ordinal regression methods, which is between classification and regression due to its ordinal label. However, existing convolutional neural network-based ordinal regression methods only focus on modifying classification head based on a randomly sampled mini-batch of data, ignoring the ordinal relationship resided in the data itself. In this paper, we propose a Meta Ordinal Weighting Network (MOW-Net) to explicitly align each training sample with a meta ordinal set (MOS) containing a few samples from all classes. During the training process, the MOW-Net learns a mapping from samples in MOS to corresponding class-specific weight. We further propose a meta cross-entropy loss to optimize the network in a meta-learning scheme. Experimental results demonstrate that the MOW-Net achieves better accuracy than the state-of-theart ordinal regression methods, especially for the unsure class.

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Section: Classification Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Meta Ordinal Weighting Net For Improving Lung Nodule Classification

Lei

Shan

Zhang

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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show abstract

“…Moreover, N − 1 weights need to be manually fine-tuned to balance the CE loss of each branch. [34,22] proposes to use a stick-breaking process to re-parametrize the outputs of N − 1 units that is associated with the Bayesian nonparametric Dirichlet process. Embarking on this, the cumulative probabilities can achieve the expected monotonical decrease, but it couldn't be unheeded that it's considerably more sophisticate than standard CE-loss.…”

Section: Introductionmentioning

confidence: 99%

Unimodal-Uniform Constrained Wasserstein Training for Medical Diagnosis

Liu

Han

Qiao

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

Self Cite

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The labels in medical diagnosis task are usually discrete and successively distributed. For example, the Diabetic Retinopathy Diagnosis (DR) involves five health risk levels: no DR (0), mild DR (1), moderate DR (2), severe DR (3) and proliferative DR (4). This labeling system is common for medical disease. Previous methods usually construct a multi-binary-classification task or propose some reparameter schemes in the output unit. In this paper, we target on this task from the perspective of loss function. More specifically, the Wasserstein distance is utilized as an alternative, explicitly incorporating the inter-class correlations by pre-defining its ground metric. Then, the ground metric which serves as a linear, convex or concave increasing function w.r.t. the Euclidean distance in a line is explored from an optimization perspective.Meanwhile, this paper also proposes of constructing the smoothed target labels that model the inlier and outlier noises by using a unimodal-uniform mixture distribution. Different from the one-hot setting, the smoothed label endues the computation of Wasserstein distance with more challenging features. With either one-hot or smoothed target label, this paper systematically concludes the practical closed-form solution. We evaluate our method on several medical diagnosis tasks (e.g., Diabetic Retinopathy and Ultrasound Breast dataset) and achieve state-of-the-art performance.

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“…Compared to the case of a single image [24,37,31,38,29,26], richer and complementary information can be expected in a set, because the samples are captured from multiple views [36]. However, it also poses several challenges including a) variable number of samples within a set, b) larger inner-set variability than its video-based recognition counterpart, and c) order-less data.…”

Section: Introductionmentioning

confidence: 99%

Permutation-Invariant Feature Restructuring for Correlation-Aware Image Set-Based Recognition

Liu

Guo

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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We consider the problem of comparing the similarity of image sets with variable-quantity, quality and un-ordered heterogeneous images. We use feature restructuring to exploit the correlations of both inner&inter-set images. Specifically, the residual self-attention can effectively restructure the features using the other features within a set to emphasize the discriminative images and eliminate the redundancy. Then, a sparse/collaborative learning-based dependency-guided representation scheme reconstructs the probe features conditional to the gallery features in order to adaptively align the two sets. This enables our framework to be compatible with both verification and open-set identification. We show that the parametric self-attention network and non-parametric dictionary learning can be trained endto-end by a unified alternative optimization scheme, and that the full framework is permutation-invariant. In the numerical experiments we conducted, our method achieves top performance on competitive image set/video-based face recognition and person re-identification benchmarks.

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Ordinal Regression with Neuron Stick-Breaking for Medical Diagnosis

Cited by 44 publications

References 19 publications

Meta Ordinal Weighting Net For Improving Lung Nodule Classification

Meta Ordinal Weighting Net For Improving Lung Nodule Classification

Unimodal-Uniform Constrained Wasserstein Training for Medical Diagnosis

Permutation-Invariant Feature Restructuring for Correlation-Aware Image Set-Based Recognition

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