ArticleJournal of imaging2022
Kidney Tumor Semantic Segmentation Using Deep Learning: A Survey of State-of-the-Art.
Article in Journal of imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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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.
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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
10 citing papers in PubMed.
- Automated Renal Tumor Segmentation in Computed Tomography Images Using a Global Attention-Based DeepLabV3+ Model: Algorithm Development and Validation.JMIR medical informatics · 2026Article
- Submanifold sparse convolutional networks for automated 3D segmentation of kidneys and kidney tumours in computed tomography.Scientific reports · 2026Article
- Kidney Segmentation of Histopathological Images with Edge-Aware U-Net to Support Medical Diagnosis and Treatment Planning.Bioengineering (Basel, Switzerland) · 2026Article
- Performance enhancement method by using the probabilistic estimation for kidney tumor segmentation.Frontiers in oncology · 2026Article
- Kidney cancer diagnosis and surgery selection by double decker convolutional neural network from CT scans combined with great wall construction algorithm.Abdominal radiology (New York) · 2025Article
- Virtual and augmented reality systems and three-dimensional printing of the renal model-novel trends to guide preoperative planning for renal cancer.Asian journal of urology · 2024Review
- RETHINKING INTERMEDIATE LAYERS DESIGN IN KNOWLEDGE DISTILLATION FOR KIDNEY AND LIVER TUMOR SEGMENTATION.ArXiv · 2024Article
- Kidney Segmentation from Dynamic Contrast-Enhanced Magnetic Resonance Imaging Integrating Deep Convolutional Neural Networks and Level Set Methods.Bioengineering (Basel, Switzerland) · 2023Article
- Kidney Cancer Diagnosis and Surgery Selection by Machine Learning from CT Scans Combined with Clinical Metadata.Cancers · 2023Article
- A framework to distinguish healthy/cancer renal CT images using the fused deep features.Frontiers in public health · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cure rates for kidney cancer vary according to stage and grade; hence, accurate diagnostic procedures for early detection and diagnosis are crucial. Some difficulties with manual segmentation have necessitated the use of deep learning models to assist clinicians in effectively recognizing and segmenting tumors. Deep learning (DL), particularly convolutional neural networks, has produced outstanding success in classifying and segmenting images. Simultaneously, researchers in the field of medical image segmentation employ DL approaches to solve problems such as tumor segmentation, cell segmentation, and organ segmentation. Segmentation of tumors semantically is critical in radiation and therapeutic practice. This article discusses current advances in kidney tumor segmentation systems based on DL. We discuss the various types of medical images and segmentation techniques and the assessment criteria for segmentation outcomes in kidney tumor segmentation, highlighting their building blocks and various strategies.
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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 Socratic graph.