ArticleScientific reports2025
Advanced transformer with attention-based neural network framework for precise renal cell carcinoma detection using histological kidney images.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
3 citing papers in PubMed.
- Predicting ki-67 expression in breast cancer via transformer and multiple instance learning on DCE-MRI.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Transformer-augmented dual-branch siamese tracker with confidence-aware regression and adaptive template updating.Scientific reports · 2026Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Renal cell carcinoma (RCC) is one of the typical categories of kidney cancer and is a varied group of malignancies arising from epithelial cells of the kidney parenchyma. RCC has more than ten subtypes. Classification of RCC sub-types is mainly according to morphologic features seen on histopathological hematoxylin and eosin (H & E)-stained slides. The histology classification of RCCs is of great significance, considering the important therapeutic and prognostic implications of its histologic subtypes. Imaging models play a prominent role in the diagnosis, follow-up, and staging of RCC. Histopathological images comprise morphological markers of disease development that have both predictive and diagnostic value. Recently, deep learning (DL) has achieved advanced performance in various computer vision tasks, including segmentation, image classification, and object detection. With the provision of sufficient data, the precision of a DL-enabled diagnosis model frequently matches or even exceeds that of qualified doctors. This paper presents an Advanced Transformer and Attention-Based Neural Network Framework for the Intelligent Detection of Renal Cell Carcinoma (ATANNF-IDRCC) model. The aim is to develop an accurate and automated model for detecting and ranking RCC using kidney histopathology images. Initially, the image pre-processing stage utilizes the contrast enhancement method to enhance the image quality. Furthermore, the ATANNF-IDRCC model utilizes the Twins-Spatially Separable Vision Transformer (Twins-SVT) method for feature extraction. For the RCC classification process, a hybrid model of bidirectional temporal convolutional network and long short-term memory with an attention mechanism (BiTCN-BiLSTM-AM) is employed. The performance analysis of the ATANNF-IDRCC technique is examined under the RCCGNet dataset. The comparison study of the ATANNF-IDRCC technique demonstrated a superior accuracy value of 98.26% compared to existing models.
Indexed as
Identifiers
What OpenQuestion holds
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.