ArticleOxidative medicine and cellular longevity2022
Automatic Detection and Segmentation of Ovarian Cancer Using a Multitask Model in Pelvic CT Images.
Article in Oxidative medicine and cellular longevity, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Diagnostic accuracy of ovarian cancer using convolutional neural network: a systematic review and meta-analysis.BMC medical informatics and decision making · 2026Pooled it
- Intelligent system based on multiple networks for accurate ovarian tumor semantic segmentation.Heliyon · 2024Article
- Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities.Polish journal of radiology · 2024Review
- Performance Analysis of Segmentation and Classification of CT-Scanned Ovarian Tumours Using U-Net and Deep Convolutional Neural Networks.Diagnostics (Basel, Switzerland) · 2023Article
- Neoadjuvant chemotherapy induces an elevation of tumour apparent diffusion coefficient values in patients with ovarian cancer.BMC cancer · 2023Article
- Retracted: Automatic Detection and Segmentation of Ovarian Cancer Using a Multitask Model in Pelvic CT Images.Oxidative medicine and cellular longevity · 2023Article
Corrections and comments
- Retraction · 2023-08-02Concerns/Issues about Data · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Computer-Aided Content or Computer-Generated Content · Unreliable Results and/or Conclusions · · See also: https://pubpeer.com/publications/A64F7CBFE1E37A59EC42CF39C5CD0C
- Retracted
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ovarian cancer is one of the most common malignant tumours of female reproductive organs in the world. The pelvic CT scan is a common examination method used for the screening of ovarian cancer, which shows the advantages in safety, efficiency, and providing high-resolution images. Recently, deep learning applications in medical imaging attract more and more attention in the research field of tumour diagnostics. However, due to the limited number of relevant datasets and reliable deep learning models, it remains a challenging problem to detect ovarian tumours on CT images. In this work, we first collected CT images of 223 ovarian cancer patients in the Affiliated Hospital of Qingdao University. A new end-to-end network based on YOLOv5 is proposed, namely, YOLO-OCv2 (ovarian cancer). We improved the previous work YOLO-OC firstly, including balanced mosaic data enhancement and decoupled detection head. Then, based on the detection model, a multitask model is proposed, which can simultaneously complete the detection and segmentation tasks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.