Semi-Supervised Robust Mixture Models in RKHS for Abnormality Detection in Medical Images

Kumar, N. V. Narendra; Awate, Suyash P.

doi:10.1109/tip.2020.2975958

Cited by 16 publications

(4 citation statements)

References 59 publications

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“…EM algorithm relies on mixture models and is a popular way to solve SSL problems and the methods have lots of successful applications in different fields, such as image processing and data classification tasks [31]- [33]. As defined in Section III-A, (X m , y m ) = {(x i , y i )} m i=1 denote the electricity usage data and their correlated labels, X n = {x j } m+n j=m+1 denote electricity usage data without labels.…”

Section: A Conventional Semi-supervised Learning Methodsmentioning

confidence: 99%

Solar Panel Identification Via Deep Semi-Supervised Learning and Deep One-Class Classification

Cook

Luo

Weng

2022

IEEE Trans. Power Syst.

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Section: A Conventional Semi-supervised Learning Methodsmentioning

confidence: 99%

Solar Panel Identification Via Deep Semi-Supervised Learning and Deep One-Class Classification

Cook

Luo

Weng

2022

IEEE Trans. Power Syst.

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“…This method outperformed many state-of-the-arts semi-supervised learning methods on ISIC 2018 challenge and thorax disease classification with Chest X-ray images. Kumar et al integrated the idea of semi-supervised learning into the model for retinopathy and cancer anomaly detection (Kumar and Awate, 2020 ). This model achieved high-quality outlier labeling by a small amount of expert calibration data.…”

Section: Introductionmentioning

confidence: 99%

Co-optimization Learning Network for MRI Segmentation of Ischemic Penumbra Tissues

Liu

Zhang

Wang

et al. 2021

Front. Neuroinform.

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Convolutional neural networks (CNNs) have brought hope for the medical image auxiliary diagnosis. However, the shortfall of labeled medical image data is the bottleneck that limits the performance improvement of supervised CNN methods. In addition, annotating a large number of labeled medical image data is often expensive and time-consuming. In this study, we propose a co-optimization learning network (COL-Net) for Magnetic Resonance Imaging (MRI) segmentation of ischemic penumbra tissues. COL-Net base on the limited labeled samples and consists of an unsupervised reconstruction network (R), a supervised segmentation network (S), and a transfer block (T). The reconstruction network extracts the robust features from reconstructing pseudo unlabeled samples, which is the auxiliary branch of the segmentation network. The segmentation network is used to segment the target lesions under the limited labeled samples and the auxiliary of the reconstruction network. The transfer block is used to co-optimization the feature maps between the bottlenecks of the reconstruction network and segmentation network. We propose a mix loss function to optimize COL-Net. COL-Net is verified on the public ischemic penumbra segmentation challenge (SPES) with two dozen labeled samples. Results demonstrate that COL-Net has high predictive accuracy and generalization with the Dice coefficient of 0.79. The extended experiment also shows COL-Net outperforms most supervised segmentation methods. COL-Net is a meaningful attempt to alleviate the limited labeled sample problem in medical image segmentation.

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“…There is a major concern that existing semi-supervised medical image classification methods ignore the interactive influence between image samples, which can be addressed by the Graph Convolutional Network (GCN) with a graph learning module [12]. There lefts a major challenge for the automatic histological image classification that is the limited amount of data available under supervised learning and temporally annotating a large number of breast histological images is unsubstantial in clinical application as demonstrated in [1], [18], [30]. Nevertheless, transfer learning provides a novel ideology that has been proved to be effective for solving the challenge of limited labeled histology data [27], [29], [33], and it needs a completely labeled dataset as the source domain with a target domain of partially labeled data.…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Breast Histological Image Classification by Node-Attention Graph Transfer Network

2020

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As a major cause of leading female death, breast cancer is often diagnosed by histological images which has been resolved by many deep learning methods with the assistance of large amounts of annotated data. However, their performances are severely limited by the lack of sufficient labeled data in clinical practice. This paper aims to relieve the annotating workload by a semi-supervised transfer learning algorithm to conduct knowledge distillation from a completely labeled source domain. To achieve this goal, we propose a node-attention graph transfer network to exploit the inherent correlation between individual samples by graph convolutional network, along with a cross-domain graph learning module to stimulate the graph construction in target domain. In the meanwhile, we design a node-attention mechanism to learn the individual contribution of each source image for target domain, which can further leverage the domain-gap by our node-attention transfer learning. Results of semi-supervised breast histological image classification with various scales of annotated training images are performable and further experiments demonstrate the significant contributions of each component we proposed.

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Semi-Supervised Robust Mixture Models in RKHS for Abnormality Detection in Medical Images

Cited by 16 publications

References 59 publications

Solar Panel Identification Via Deep Semi-Supervised Learning and Deep One-Class Classification

Solar Panel Identification Via Deep Semi-Supervised Learning and Deep One-Class Classification

Co-optimization Learning Network for MRI Segmentation of Ischemic Penumbra Tissues

Semi-Supervised Breast Histological Image Classification by Node-Attention Graph Transfer Network

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