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Pingkun Yan

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6 papers

1 author row

AAAI Conference 2023 Conference Paper

When Neural Networks Fail to Generalize? A Model Sensitivity Perspective

Jiajin Zhang
Hanqing Chao
Amit Dhurandhar
Pin-Yu Chen
Ali Tajer
Yangyang Xu
Pingkun Yan

Domain generalization (DG) aims to train a model to perform well in unseen domains under different distributions. This paper considers a more realistic yet more challenging scenario, namely Single Domain Generalization (Single-DG), where only a single source domain is available for training. To tackle this challenge, we first try to understand when neural networks fail to generalize? We empirically ascertain a property of a model that correlates strongly with its generalization that we coin as "model sensitivity". Based on our analysis, we propose a novel strategy of Spectral Adversarial Data Augmentation (SADA) to generate augmented images targeted at the highly sensitive frequencies. Models trained with these hard-to-learn samples can effectively suppress the sensitivity in the frequency space, which leads to improved generalization performance. Extensive experiments on multiple public datasets demonstrate the superiority of our approach, which surpasses the state-of-the-art single-DG methods by up to 2.55%. The source code is available at https://github.com/DIAL-RPI/Spectral-Adversarial-Data-Augmentation.

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JBHI Journal 2021 Journal Article

Multi-Task Learning for Registering Images With Large Deformation

Bo Du
Jiandong Liao
Baris Turkbey
Pingkun Yan

Accurate registration of prostate magnetic resonance imaging (MRI) images of the same subject acquired at different time points helps diagnose cancer and monitor the tumor progress. However, it is very challenging especially when one image was acquired with the use of endorectal coil (ERC) but the other was not, which causes significant deformation. Classical iterative image registration methods are also computationally intensive. Deep learning based registration frameworks have recently been developed and demonstrated promising performance. However, the lack of proper constraints often results in unrealistic registration. In this paper, we propose a multi-task learning based registration network with anatomical constraint to address these issues. The proposed approach uses a cycle constraint loss to achieve forward/backward registration and an inverse constraint loss to encourage diffeomorphic registration. In addition, an adaptive anatomical constraint aiming for regularizing the registration network with the use of anatomical labels is introduced through weak supervision. Our experiments on registering prostate MR images of the same subject obtained at different time points with and without ERC show that the proposed method achieves very promising performance under different measures in dealing with the large deformation. Compared with other existing methods, our approach works more efficiently with average running time less than a second and is able to obtain more visually realistic results.

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JBHI Journal 2020 Journal Article

Knowledge-Based Analysis for Mortality Prediction From CT Images

Hengtao Guo
Uwe Kruger
Ge Wang
Mannudeep K. Kalra
Pingkun Yan

Low-Dose CT (LDCT) can significantly improve the accuracy of lung cancer diagnosis and thus reduce cancer deaths compared to chest X-ray. The lung cancer risk population is also at high risk of other deadly diseases, for instance, cardiovascular diseases. Therefore, predicting the all-cause mortality risks of this population is of great importance. This paper introduces a knowledge-based analytical method using deep convolutional neural network (CNN) for all-cause mortality prediction. The underlying approach combines structural image features extracted from CNNs, based on LDCT volume at different scales, and clinical knowledge obtained from quantitative measurements, to predict the mortality risk of lung cancer screening subjects. The proposed method is referred as Knowledge-based Analysis of Mortality Prediction Network (KAMP-Net). It constitutes a collaborative framework that utilizes both imaging features and anatomical information, instead of completely relying on automatic feature extraction. Our work demonstrates the feasibility of incorporating quantitative clinical measurements to assist CNNs in all-cause mortality prediction from chest LDCT images. The results of this study confirm that radiologist defined features can complement CNNs in performance improvement. The experiments demonstrate that KAMP-Net can achieve a superior performance when compared to other methods.

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IJCAI Conference 2020 Conference Paper

Unsupervised Domain Adaptation with Dual-Scheme Fusion Network for Medical Image Segmentation

Danbing Zou
Qikui Zhu
Pingkun Yan

Domain adaptation aims to alleviate the problem of retraining a pre-trained model when applying it to a different domain, which requires large amount of additional training data of the target domain. Such an objective is usually achieved by establishing connections between the source domain labels and target domain data. However, this imbalanced source-to-target one way pass may not eliminate the domain gap, which limits the performance of the pre-trained model. In this paper, we propose an innovative Dual-Scheme Fusion Network (DSFN) for unsupervised domain adaptation. By building both source-to-target and target-to-source connections, this balanced joint information flow helps reduce the domain gap to further improve the network performance. The mechanism is further applied to the inference stage, where both the original input target image and the generated source images are segmented with the proposed joint network. The results are fused to obtain more robust segmentation. Extensive experiments of unsupervised cross-modality medical image segmentation are conducted on two tasks -- brain tumor segmentation and cardiac structures segmentation. The experimental results show that our method achieved significant performance improvement over other state-of-the-art domain adaptation methods.

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JBHI Journal 2019 Journal Article

MR Image Super-Resolution via Wide Residual Networks With Fixed Skip Connection

Jun Shi
Zheng Li
Shihui Ying
Chaofeng Wang
Qingping Liu
Qi Zhang
Pingkun Yan

Spatial resolution is a critical imaging parameter in magnetic resonance imaging. The image super-resolution (SR) is an effective and cost efficient alternative technique to improve the spatial resolution of MR images. Over the past several years, the convolutional neural networks (CNN)-based SR methods have achieved state-of-the-art performance. However, CNNs with very deep network structures usually suffer from the problems of degradation and diminishing feature reuse, which add difficulty to network training and degenerate the transmission capability of details for SR. To address these problems, in this work, a progressive wide residual network with a fixed skip connection (named FSCWRN) based SR algorithm is proposed to reconstruct MR images, which combines the global residual learning and the shallow network based local residual learning. The strategy of progressive wide networks is adopted to replace deeper networks, which can partially relax the above-mentioned problems, while a fixed skip connection helps provide rich local details at high frequencies from a fixed shallow layer network to subsequent networks. The experimental results on one simulated MR image database and three real MR image databases show the effectiveness of the proposed FSCWRN SR algorithm, which achieves improved reconstruction performance compared with other algorithms.

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JBHI Journal 2014 Journal Article

Adaptive Shape Prior Constrained Level Sets for Bladder MR Image Segmentation

Xianjing Qin
Xuelong Li
Yang Liu
Hongbing Lu
Pingkun Yan

Three-dimensional bladder wall segmentation for thickness measuring can be very useful for bladder magnetic resonance (MR) image analysis, since thickening of the bladder wall can indicate abnormality. However, it is a challenging task due to the artifacts inside bladder lumen, weak boundaries in the apex and base areas, and complicated outside intensity distributions. To deal with these difficulties, in this paper, an adaptive shape prior constrained directional level set model is proposed to segment the inner and outer boundaries of the bladder wall. In addition, a coupled directional level set model is presented to refine the segmentation by exploiting the prior knowledge of region information and minimum thickness. With our proposed method, the influence of the artifacts in the bladder lumen and the complicated outside tissues surrounding the bladder can be appreciably reduced. Furthermore, the leakage on the weak boundaries can be avoided. Compared with other related methods, better results were obtained on 11 patients' 3-D bladder MR images by using the proposed method.

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