Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet

Zhu, Chuang; Mei, Ke; Peng, Ting; Luo, Yihao; Li, Jun; Wang, Ying; Jin, Mulan

doi:10.1016/j.neucom.2020.04.154

Cited by 21 publications

(13 citation statements)

References 24 publications

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“…Currently, DL has enabled rapid advances in computational pathology ( 11 , 12 ). For example, DL methods have been applied to segment and classify glomeruli with different staining and various pathologic changes, thus achieving the automatic analysis of renal biopsies ( 13 , 14 ); meanwhile, DL-based automatic colonoscopy tissue segmentation and classification have shown promise for colorectal cancer detection ( 15 , 16 ); besides, the analysis of gastric carcinoma and precancerous status can also benefit from DL schemes ( 17 , 18 ). More recently, for the ALN metastasis detection, it is reported that DL algorithms on digital lymph node pathology images achieved better diagnostic efficiency of ALN metastasis than pathologists ( 19 , 20 ).…”

Section: Introductionmentioning

confidence: 99%

Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides

Zhu

Tang

et al. 2021

Front. Oncol.

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ObjectivesTo develop and validate a deep learning (DL)-based primary tumor biopsy signature for predicting axillary lymph node (ALN) metastasis preoperatively in early breast cancer (EBC) patients with clinically negative ALN.MethodsA total of 1,058 EBC patients with pathologically confirmed ALN status were enrolled from May 2010 to August 2020. A DL core-needle biopsy (DL-CNB) model was built on the attention-based multiple instance-learning (AMIL) framework to predict ALN status utilizing the DL features, which were extracted from the cancer areas of digitized whole-slide images (WSIs) of breast CNB specimens annotated by two pathologists. Accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curves, and areas under the ROC curve (AUCs) were analyzed to evaluate our model.ResultsThe best-performing DL-CNB model with VGG16_BN as the feature extractor achieved an AUC of 0.816 (95% confidence interval (CI): 0.758, 0.865) in predicting positive ALN metastasis in the independent test cohort. Furthermore, our model incorporating the clinical data, which was called DL-CNB+C, yielded the best accuracy of 0.831 (95%CI: 0.775, 0.878), especially for patients younger than 50 years (AUC: 0.918, 95%CI: 0.825, 0.971). The interpretation of DL-CNB model showed that the top signatures most predictive of ALN metastasis were characterized by the nucleus features including density (p = 0.015), circumference (p = 0.009), circularity (p = 0.010), and orientation (p = 0.012).ConclusionOur study provides a novel DL-based biomarker on primary tumor CNB slides to predict the metastatic status of ALN preoperatively for patients with EBC.

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Section: Introductionmentioning

confidence: 99%

Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides

Zhu

Tang

et al. 2021

Front. Oncol.

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“…It also inspired a large number of researchers to think about U-shaped semantic segmentation networks. In the field of natural image understanding, an increasing number of semantic segmentation and target detection SOTA models have begun to pay attention to and use U-shaped structures [ 39 – 41 ].…”

Section: Discussionmentioning

confidence: 99%

Deep learning as a novel method for endoscopic diagnosis of chronic atrophic gastritis: a prospective nested case–control study

2022

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Background and aims Chronic atrophic gastritis (CAG) is a precancerous disease that often leads to the development of gastric cancer (GC) and is positively correlated with GC morbidity. However, the sensitivity of the endoscopic diagnosis of CAG is only 42%. Therefore, we developed a real-time video monitoring model for endoscopic diagnosis of CAG based on U-Net deep learning (DL) and conducted a prospective nested case–control study to evaluate the diagnostic evaluation indices of the model and its consistency with pathological diagnosis. Methods Our cohort consisted of 1539 patients undergoing gastroscopy from December 1, 2020, to July 1, 2021. Based on pathological diagnosis, patients in the cohort were divided into the CAG group or the chronic nonatrophic gastritis (CNAG) group, and we assessed the diagnostic evaluation indices of this model and its consistency with pathological diagnosis after propensity score matching (PSM) to minimize selection bias in the study. Results After matching, the diagnostic evaluation indices and consistency evaluation of the model were better than those of endoscopists [sensitivity (84.02% vs. 62.72%), specificity (97.04% vs. 81.95%), positive predictive value (96.60% vs. 77.66%), negative predictive value (85.86% vs. 68.73%), accuracy rate (90.53% vs. 72.34%), Youden index (81.06% vs. 44.67%), odd product (172.5 vs. 7.64), positive likelihood ratio (28.39 vs. 3.47), negative likelihood ratio (0.16 vs. 0.45), AUC (95% CI) [0.909 (0.884–0.934) vs. 0.740 (0.702–0.778)] and Kappa (0.852 vs. 0.558)]. Conclusions Our prospective nested case–control study proved that the diagnostic evaluation indices and consistency evaluation of the real-time video monitoring model for endoscopic diagnosis of CAG based on U-Net DL were superior to those of endoscopists. Trial registrationChiCTR2100044458, 18/03/2020.

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“…Utilizing pair-wise potentials, however, may cause serious boundary leakage, especially in low-contrast regions ( Vijayanarasimhan and Grauman, 2010 ). To prevent leakage and the lack of spatial consistency, methods such as patch-based networks for training CNNs and multi-scale, multi-path CNNs with different input resolutions/network architectures have been used ( Pereira et al, 2016a ; Havaei et al, 2017 ; Kamnitsas et al, 2017 ; Chattopadhay et al, 2018 ; Xiao et al, 2020 ; Ghimire et al, 2021 ; Zhang et al, 2021 ; Zhu et al, 2021 ). However, patch-based training is computationally costly.…”

Section: Introductionmentioning

confidence: 99%

“…This adversarial loss serves as an adaptively learned similarity measure between the predicted segmentation label maps and the annotated ground truth that improves localization accuracy while enforcing spatial contiguity at low contrast regions, including image boundaries. Various end-to-end adversarial neural networks (e.g., SegAN) have been proposed as stable and effective frameworks for automatic segmentation (SegAN) of organs such as the brain, chest, and abdomen, among others ( Frid-Adar et al, 2018 ; Giacomello et al, 2020 ; Xun et al, 2021 ; Zhu et al, 2021 ). Furthermore, a recent study by Chen et al (2022) showed that a GAN-based paradigm improved the robustness and generalizability of deep learning models like graph neural networks (GNNs).…”

Section: Introductionmentioning

confidence: 99%

FetalGAN: Automated Segmentation of Fetal Functional Brain MRI Using Deep Generative Adversarial Learning and Multi-Scale 3D U-Net

Asis-Cruz

Jose

et al. 2022

Front. Neurosci.

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An important step in the preprocessing of resting state functional magnetic resonance images (rs-fMRI) is the separation of brain from non-brain voxels. Widely used imaging tools such as FSL’s BET2 and AFNI’s 3dSkullStrip accomplish this task effectively in children and adults. In fetal functional brain imaging, however, the presence of maternal tissue around the brain coupled with the non-standard position of the fetal head limit the usefulness of these tools. Accurate brain masks are thus generated manually, a time-consuming and tedious process that slows down preprocessing of fetal rs-fMRI. Recently, deep learning-based segmentation models such as convolutional neural networks (CNNs) have been increasingly used for automated segmentation of medical images, including the fetal brain. Here, we propose a computationally efficient end-to-end generative adversarial neural network (GAN) for segmenting the fetal brain. This method, which we call FetalGAN, yielded whole brain masks that closely approximated the manually labeled ground truth. FetalGAN performed better than 3D U-Net model and BET2: FetalGAN, Dice score = 0.973 ± 0.013, precision = 0.977 ± 0.015; 3D U-Net, Dice score = 0.954 ± 0.054, precision = 0.967 ± 0.037; BET2, Dice score = 0.856 ± 0.084, precision = 0.758 ± 0.113. FetalGAN was also faster than 3D U-Net and the manual method (7.35 s vs. 10.25 s vs. ∼5 min/volume). To the best of our knowledge, this is the first successful implementation of 3D CNN with GAN on fetal fMRI brain images and represents a significant advance in fully automating processing of rs-MRI images.

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Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet

Cited by 21 publications

References 24 publications

Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides

Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides

Deep learning as a novel method for endoscopic diagnosis of chronic atrophic gastritis: a prospective nested case–control study

FetalGAN: Automated Segmentation of Fetal Functional Brain MRI Using Deep Generative Adversarial Learning and Multi-Scale 3D U-Net

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