ArticleCancers2022
Two-Stage Deep Learning Model for Automated Segmentation and Classification of Splenomegaly.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
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Who cites it
8 citing papers in PubMed, 14 citations in OpenAlex.
- Multimodal clinical-imaging deep learning model for predicting refractory hypersplenism after liver transplantation.Scientific reports · 2026Article
- Deep Learning Model for Automated Segmentation of Orbital Structures in MRI Images.Clinical neuroradiology · 2025Article
- A dataset for splenomegaly and its related findings in CT imaging.Data in brief · 2025Article
- Automated liver and spleen segmentation for MR elastography maps using U-Nets.Scientific reports · 2025Article
- Deep learning and radiomics for gastric cancer serosal invasion: automated segmentation and multi-machine learning from two centers.Journal of cancer research and clinical oncology · 2025Article
- Deep learning-enabled detection of hypoxic-ischemic encephalopathy after cardiac arrest in CT scans: a comparative study of 2D and 3D approaches.Frontiers in neuroscience · 2024Article
- A Deep-Learning Approach to Spleen Volume Estimation in Patients with Gaucher Disease.Journal of clinical medicine · 2023Article
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Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Splenomegaly is a common cross-sectional imaging finding with a variety of differential diagnoses. This study aimed to evaluate whether a deep learning model could automatically segment the spleen and identify the cause of splenomegaly in patients with cirrhotic portal hypertension versus patients with lymphoma disease. This retrospective study included 149 patients with splenomegaly on computed tomography (CT) images (77 patients with cirrhotic portal hypertension, 72 patients with lymphoma) who underwent a CT scan between October 2020 and July 2021. The dataset was divided into a training (
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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.