A Transfer Learning-Based Multi-Instance Learning Method With Weak Labels

Xiao, Yanshan; Liang, Fei; Liu, Bo

doi:10.1109/tcyb.2020.2973450

Cited by 27 publications

(9 citation statements)

References 58 publications

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“…MIL, in which labels are associated with bags rather than the instances in the bag, greatly reduces label requirement while CNN is a fully supervised deep learning model that asks for fully labeled samples for training. [34][35][36][37] LSTM is one special type of recurrent neural networks (RNNs),and it has better control in long-term memory to reduce the signal loss during the process of conventional RNN architectures and to provide spatial information among layers. 18,38,39 Thus, the combination of those two algorithms allowed 3DMTM to extract more spatial information with high SNR from targeted lesion without any manual annotation.…”

Section: Discussionmentioning

confidence: 99%

An original deep learning model using limited data for COVID‐19 discrimination: A multicenter study

Lou

Chen

et al. 2022

Medical Physics

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Objectives Artificial intelligence (AI) has been proved to be a highly efficient tool for COVID‐19 diagnosis, but the large data size and heavy label force required for algorithm development and the poor generalizability of AI algorithms, to some extent, limit the application of AI technology in clinical practice. The aim of this study is to develop an AI algorithm with high robustness using limited chest CT data for COVID‐19 discrimination. Methods A three dimensional algorithm that combined multi‐instance learning with the LSTM architecture (3DMTM) was developed for differentiating COVID‐19 from community acquired pneumonia (CAP) while logistic regression (LR), k‐nearest neighbor (KNN), support vector machine (SVM), and a three dimensional convolutional neural network set for comparison. Totally, 515 patients with or without COVID‐19 between December 2019 and March 2020 from five different hospitals were recruited and divided into relatively large (150 COVID‐19 and 183 CAP cases) and relatively small datasets (17 COVID‐19 and 35 CAP cases) for either training or validation and another independent dataset (37 COVID‐19 and 93 CAP cases) for external test. Area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, accuracy, F1 score, and G‐mean were utilized for performance evaluation. Results In the external test cohort, the relatively large data‐based 3DMTM‐LD achieved an AUC of 0.956 (95% confidence interval, 95% CI, 0.929∼0.982) with 86.2% and 98.0% for its sensitivity and specificity. 3DMTM‐SD got an AUC of 0.937 (95% CI, 0.909∼0.965), while the AUC of 3DCM‐SD decreased dramatically to 0.714 (95% CI, 0.649∼0.780) with training data reduction. KNN‐MMSD, LR‐MMSD, SVM‐MMSD, and 3DCM‐MMSD benefited significantly from the inclusion of clinical information while models trained with relatively large dataset got slight performance improvement in COVID‐19 discrimination. 3DMTM, trained with either CT or multi‐modal data, presented comparably excellent performance in COVID‐19 discrimination. Conclusions The 3DMTM algorithm presented excellent robustness for COVID‐19 discrimination with limited CT data. 3DMTM based on CT data performed comparably in COVID‐19 discrimination with that trained with multi‐modal information. Clinical information could improve the performance of KNN, LR, SVM, and 3DCM in COVID‐19 discrimination, especially in the scenario with limited data for training.

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Section: Discussionmentioning

confidence: 99%

An original deep learning model using limited data for COVID‐19 discrimination: A multicenter study

Lou

Chen

et al. 2022

Medical Physics

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“…The development of deep neural networks [22], [23] has significantly accelerated the progress of computer vision tasks [24], [25]. Generative models [26] are widely used in many areas because of their powerful modeling capabilities based on accurately modeled distributions of image characteristics.…”

Section: B Deep Generative Methodsmentioning

confidence: 99%

Regionwise Generative Adversarial Image Inpainting for Large Missing Areas

Liu

Bai

et al. 2023

IEEE Trans. Cybern.

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Recently deep neural networks have achieved promising performance for in-filling large missing regions in image inpainting tasks. They have usually adopted the standard convolutional architecture over the corrupted image, leading to meaningless contents, such as color discrepancy, blur and other artifacts. Moreover, most inpainting approaches cannot handle well the case of a large contiguous missing area. To address these problems, we propose a generic inpainting framework capable of handling incomplete images with both contiguous and discontiguous large missing areas. We pose this in an adversarial manner, deploying region-wise operations in both the generator and discriminator to separately handle the different types of regions, namely existing regions and missing ones. Moreover, a correlation loss is introduced to capture the non-local correlations between different patches, and thus guide the generator to obtain more information during inference. With the help of region-wise generative adversarial mechanism, our framework can restore semantically reasonable and visually realistic images for both discontiguous and contiguous large missing areas. Extensive experiments on three widely-used datasets for image inpainting task have been conducted, and both qualitative and quantitative experimental results demonstrate that the proposed model significantly outperforms the state-of-the-art approaches, on the large contiguous and discontiguous missing areas.

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“…Transfer learning is to apply the knowledge or patterns learned from a certain field or task (called the source domain) to a different but related fields or problems (called the target domain).The onerous work of labeling a sea of data during network training can be avoided through transfer learning (Mensink et al 2022, Xiao et al 2022. In addition, the initial performance of the network is better, the rate of model upgrading is faster, and the efficiency of the obtained network is improved, which is very helpful for pathological image processing.…”

Section: Dual Transfer Learningmentioning

confidence: 99%

A generalized AI method for pathology cancer diagnosis and prognosis prediction based on transfer learning and hierarchical split

et al. 2023

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Objective. This study aims to propose a generalized AI method for pathology cancer diagnosis and prognosis prediction based on transfer learning and hierarchical split. Approach. We present a neural network framework for cancer diagnosis and prognosis prediction in pathological images. To enhance the network's depth and width, we employ a hierarchical split block (HS-Block) to create an AI-aided diagnosis system suitable for semi-supervised clinical settings with limited labeled samples and cross-domain tasks. By incorporating a lightweight convolution unit based on the HS-Block, we improve the feature information extraction capabilities of a regular network (RegNet). Additionally, we integrate a Convolutional Block Attention Module (CBAM) into the first and last convolutions to optimize the extraction of global features and local details. To address limited sample labels, we employ a dual-transfer learning (DTL) mechanism named DTL-HS-Regnet, enabling semi-supervised learning in clinical settings. Main results. Our proposed DTL-HS-Regnet model outperforms other advanced deep-learning models in three different types of cancer diagnosis tasks. It demonstrates superior feature extraction ability, achieving an average sensitivity, specificity, accuracy, and F1 score of 0.9987, 1.0000, 1.0000, and 0.9992, respectively. Furthermore, we evaluate the model's capability to directly extract prognosis prediction information from pathological images by constructing patient cohorts. The results show that the correlation between DTL-HS-Regnet predictions and the presence of cancer-associated fibroblasts (CAF) is comparable to that of pathologists. Significance. Our proposed AI method offers a generalized approach for cancer diagnosis and prognosis prediction in pathology. The outstanding performance of the DTL-HS-Regnet model demonstrates its potential for improving current practices in image digital pathology, expanding the boundaries of cancer treatment in two critical areas.

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A Transfer Learning-Based Multi-Instance Learning Method With Weak Labels

Cited by 27 publications

References 58 publications

An original deep learning model using limited data for COVID‐19 discrimination: A multicenter study

An original deep learning model using limited data for COVID‐19 discrimination: A multicenter study

Regionwise Generative Adversarial Image Inpainting for Large Missing Areas

A generalized AI method for pathology cancer diagnosis and prognosis prediction based on transfer learning and hierarchical split

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