Semi-Supervised Multi-Domain Multi-Task Training for Metastatic Colon Lymph Node Diagnosis from Abdominal CT

Glaser, Saskia; Maicas, Gabriel; Bedrikovetski, Sergei; Sammour, Tarik; Carneiro, Gustavo

doi:10.1109/isbi45749.2020.9098372

Cited by 8 publications

(2 citation statements)

References 15 publications

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“…Somewhat surprisingly, our preliminary deep learning model predicted the LN stage in patients with colon cancer with a higher AUROC (0.860 vs 0.486) than the present study 24 . The discrepancy in diagnostic performance can be attributed to weak points of the preliminary study which included a smaller sample size (123 vs 1201 in the present study) and differences in deep learning architecture (DenseNet 25 vs ResNet‐50 in the present study).…”

Section: Discussioncontrasting

confidence: 72%

Deep learning to predict lymph node status on pre‐operative staging CT in patients with colon cancer

Bedrikovetski,

Zhang,

Seow

et al. 2023

J Med Imag Rad Onc

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IntroductionLymph node (LN) metastases are an important determinant of survival in patients with colon cancer, but remain difficult to accurately diagnose on preoperative imaging. This study aimed to develop and evaluate a deep learning model to predict LN status on preoperative staging CT.MethodsIn this ambispective diagnostic study, a deep learning model using a ResNet‐50 framework was developed to predict LN status based on preoperative staging CT. Patients with a preoperative staging abdominopelvic CT who underwent surgical resection for colon cancer were enrolled. Data were retrospectively collected from February 2007 to October 2019 and randomly separated into training, validation, and testing cohort 1. To prospectively test the deep learning model, data for testing cohort 2 was collected from October 2019 to July 2021. Diagnostic performance measures were assessed by the AUROC.ResultsA total of 1,201 patients (median [range] age, 72 [28–98 years]; 653 [54.4%] male) fulfilled the eligibility criteria and were included in the training (n = 401), validation (n = 100), testing cohort 1 (n = 500) and testing cohort 2 (n = 200). The deep learning model achieved an AUROC of 0.619 (95% CI 0.507–0.731) in the validation cohort. In testing cohort 1 and testing cohort 2, the AUROC was 0.542 (95% CI 0.489–0.595) and 0.486 (95% CI 0.403–0.568), respectively.ConclusionA deep learning model based on a ResNet‐50 framework does not predict LN status on preoperative staging CT in patients with colon cancer.

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Section: Discussioncontrasting

confidence: 72%

Deep learning to predict lymph node status on pre‐operative staging CT in patients with colon cancer

Bedrikovetski,

Zhang,

Seow

et al. 2023

J Med Imag Rad Onc

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“…They use geostatistical simulations to generate equiprobable texture of the original images, and then used these processed textures to train a basic backbone network to make predictions. Glaser 13 et al noted that the lack of annotation for the localization of ROIs containing LNs and the small size of the associated ROIs limit the classification accuracy of deep learning methods. They propose a semi-supervised multi-domain multi-task training to improve the diagnostic accuracy of globally annotated datasets by incorporating ROI annotations from different domains.…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised multiple image transformation network for metastatic lymph node diagnosis from CT

Hu²,

Lu³

2022

Fourteenth International Conference on Digital Image Processing (ICDIP 2022)

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Effective treatment of lung cancer requires accurate diagnosis of mediastinal lymph node metastasis (LNM). In the current clinical practices, invasive examination is considered the gold standard, but it is inefficient and probably causes complications to the patient. Therefore, the automatic diagnosis of LNM from computed tomography (CT) images based on Deep Learning (DL) methods has become important research in aided diagnosis. DL methods require a large number of high-quality data to achieve good results. However, obtaining labels for LNM is difficult, the lack of annotations for LNM limits the accuracy of deep learning network classification. In this paper, we propose a semi-supervised multiple image transformation network (MITNet) for LNM prediction in CT images. We perform multiple image transformations on the images and input them to the feature extractors to extract multi-dimensional features, then use an attention-based module (ABM) to adaptively fuse the features to accurately predict LNM. In addition, in order to solve the problem of insufficient data volume, we introduce a semi-supervised learning strategy to train the network with CT image containing only lymph node (LN) segmentation annotations to improve its generalization ability. Experimental results show that our proposed method has an accuracy of 92.45% and outperforms several state-of-the-art methods.

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Lymph Node Metastasis Classification Based on Semi-Supervised Multi-View Network

Luo

Xin

Liu

et al. 2020

2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Semi-Supervised Multi-Domain Multi-Task Training for Metastatic Colon Lymph Node Diagnosis from Abdominal CT

Cited by 8 publications

References 15 publications

Deep learning to predict lymph node status on pre‐operative staging CT in patients with colon cancer

Deep learning to predict lymph node status on pre‐operative staging CT in patients with colon cancer

Semi-supervised multiple image transformation network for metastatic lymph node diagnosis from CT

Lymph Node Metastasis Classification Based on Semi-Supervised Multi-View Network

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