ArticleCancers2023
APESTNet with Mask R-CNN for Liver Tumor Segmentation and Classification.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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.
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Who cites it
15 citing papers in PubMed, 61 citations in OpenAlex.
- Convolutional Neural Networks in Radiology: Principles, Clinical Applications, and a Practical Framework for Radiologists.Medical principles and practice : international journal of the Kuwait University, Health Science Centre · 2026Review
- Liver Tumor Segmentation with Deep Learning: A Comparative Analysis of CNN-, Transformer-, and YOLO-Based Models on the ATLAS MRI.Diagnostics (Basel, Switzerland) · 2026Article
- Deep learning techniques for using computed tomography imaging for hepatocellular carcinoma diagnosis, treatment and prognosis.World journal of gastroenterology · 2026Review
- Leveraging deep learning and explainable AI for effective liver tumor classification from CT scan images.Frontiers in oncology · 2026Article
- FSS-ULivR: a clinically-inspired few-shot segmentation framework for liver imaging using unified representations and attention mechanisms.Journal of cancer research and clinical oncology · 2025Article
- AI-Powered Object Detection in Radiology: Current Models, Challenges, and Future Direction.Journal of imaging · 2025Review
- ProtoSAM-3D: Interactive semantic segmentation in volumetric medical imaging via a Segment Anything Model and mask-level prototypes.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2025Article
- Predicting malignancy in breast lesions: enhancing accuracy with fine-tuned convolutional neural network models.BMC medical imaging · 2024Article
- Automated segmentation of liver and hepatic vessels on portal venous phase computed tomography images using a deep learning algorithm.Journal of applied clinical medical physics · 2024Article
- CustomizedHeliyon · 2024Article
- Article
- Artificial intelligence techniques in liver cancer.Frontiers in oncology · 2024Review
- Deep Learning Methods in Medical Image-Based Hepatocellular Carcinoma Diagnosis: A Systematic Review and Meta-Analysis.Cancers · 2023Review
- Usefulness of T2-Weighted Images with Deep-Learning-Based Reconstruction in Nasal Cartilage.Diagnostics (Basel, Switzerland) · 2023Article
- En-DeNet Based Segmentation and Gradational Modular Network Classification for Liver Cancer Diagnosis.Biomedicines · 2023Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diagnosis and treatment of hepatocellular carcinoma or metastases rely heavily on accurate segmentation and classification of liver tumours. However, due to the liver tumor's hazy borders and wide range of possible shapes, sizes, and positions, accurate and automatic tumour segmentation and classification remains a difficult challenge. With the advancement of computing, new models in artificial intelligence have evolved. Following its success in Natural language processing (NLP), the transformer paradigm has been adopted by the computer vision (CV) community of the NLP. While there are already accepted approaches to classifying the liver, especially in clinical settings, there is room for advancement in terms of their precision. This paper makes an effort to apply a novel model for segmenting and classifying liver tumours built on deep learning. In order to accomplish this, the created model follows a three-stage procedure consisting of (a) pre-processing, (b) liver segmentation, and (c) classification. In the first phase, the collected Computed Tomography (CT) images undergo three stages of pre-processing, including contrast improvement via histogram equalization and noise reduction via the median filter. Next, an enhanced mask region-based convolutional neural networks (Mask R-CNN) model is used to separate the liver from the CT abdominal image. To prevent overfitting, the segmented picture is fed onto an Enhanced Swin Transformer Network with Adversarial Propagation (APESTNet). The experimental results prove the superior performance of the proposed perfect on a wide variety of CT images, as well as its efficiency and low sensitivity to noise.
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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.