ArticleBioengineering (Basel, Switzerland)2023
AI-Driven Robust Kidney and Renal Mass Segmentation and Classification on 3D CT Images.
Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
- Diagnostic performance of artificial intelligence in detection of renal cell carcinoma: a systematic review and meta-analysis.BMC cancer · 2025Pooled it
- Multimodal MRI-based two-stage artificial intelligence framework for renal fibrosis classification in chronic kidney disease.BMC nephrology · 2026Article
- UCSF RMaC: University of California San Francisco 3D Multi-Phase Renal Mass CT Dataset with Tumor Segmentations.medRxiv : the preprint server for health sciences · 2026Article
- A two-stage deep learning framework for kidney disease detection using modified specular-free imaging and EfficientNetB2.Scientific reports · 2026Article
- Automated segmentation of lymph node metastases in colorectal cancer based on convolutional KAN.Open medicine (Warsaw, Poland) · 2026Article
- Advanced kidney mass segmentation using VHUCS-Net with protuberance detection network.Frontiers in artificial intelligence · 2026Article
- DiagNeXt: A Two-Stage Attention-Guided ConvNeXt Framework for Kidney Pathology Segmentation and Classification.Journal of imaging · 2025Article
- A phase-aware Cross-Scale U-MAMba with uncertainty-aware segmentation and Switch Atrous Bifovea EfficientNetB7 classification of kidney lesion subtype.Lasers in medical science · 2025Article
- A Robust Deep Learning Method with Uncertainty Estimation for the Pathological Classification of Renal Cell Carcinoma Based on CT Images.Journal of imaging informatics in medicine · 2025Article
- Special Issue: Artificial Intelligence in Advanced Medical Imaging.Bioengineering (Basel, Switzerland) · 2024Article
- CNN-Based Kidney Segmentation Using a Modified CLAHE Algorithm.Sensors (Basel, Switzerland) · 2024Article
- ASD-Net: a novel U-Net based asymmetric spatial-channel convolution network for precise kidney and kidney tumor image segmentation.Medical & biological engineering & computing · 2024Article
- [Intervention planning in modern renal surgery].Urologie (Heidelberg, Germany) · 2023Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Early intervention in kidney cancer helps to improve survival rates. Abdominal computed tomography (CT) is often used to diagnose renal masses. In clinical practice, the manual segmentation and quantification of organs and tumors are expensive and time-consuming. Artificial intelligence (AI) has shown a significant advantage in assisting cancer diagnosis. To reduce the workload of manual segmentation and avoid unnecessary biopsies or surgeries, in this paper, we propose a novel end-to-end AI-driven automatic kidney and renal mass diagnosis framework to identify the abnormal areas of the kidney and diagnose the histological subtypes of renal cell carcinoma (RCC). The proposed framework first segments the kidney and renal mass regions by a 3D deep learning architecture (Res-UNet), followed by a dual-path classification network utilizing local and global features for the subtype prediction of the most common RCCs: clear cell, chromophobe, oncocytoma, papillary, and other RCC subtypes. To improve the robustness of the proposed framework on the dataset collected from various institutions, a weakly supervised learning schema is proposed to leverage the domain gap between various vendors via very few CT slice annotations. Our proposed diagnosis system can accurately segment the kidney and renal mass regions and predict tumor subtypes, outperforming existing methods on the KiTs19 dataset. Furthermore, cross-dataset validation results demonstrate the robustness of datasets collected from different institutions trained via the weakly supervised learning schema.
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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.