Article

A Clinically Applicable and Generalizable Deep Learning Model for Anterior Mediastinal Tumors in CT Images Across Multiple Institutions

2025-07-22

Abstract excerpt

<title>Abstract</title> <p>Rare diseases are often difficult to diagnose, and their scarcity also makes it challenging to develop deep learning models for them due to lim- ited large-scale datasets. Anterior mediastinal tumors—including thymoma and thymic carcinoma—represent such rare entities. A few diagnostic support systems for these tumors have been proposed; however, no prior studies have tested them across...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 3e0f5cf3-e68d-5125-a88e-ffaf87f6eded
DOI: 10.21203/rs.3.rs-6694793/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

A Clinically Applicable and Generalizable Deep Learning Model for Anterior Mediastinal Tumors in CT Images Across Multiple InstitutionsDOI 10.21203/rs.3.rs-6694793/v1Read

Development and Validation of A Diagnostic Nomogram Based on PET/CT and Clinical Characteristics: Differentiating mediastinal lymphomas from aggressive thymic epithelial tumorsSemantic proximity Read

Can Computed Tomography-Based Radiomics Potentially Discriminate Between Anterior Mediastinal Cysts and type B1 and B2 thymomas?Semantic proximity Read

Development and Validation of a Deep Learning Model for Preoperative Screening of Myasthenia Gravis in Patients with Thymoma based on CT ImagesSemantic proximity Read

Comparison of the conventional and small-field-of-view CT scan for diagnosing sub-solid nodules using a multi-instance deep learning method：a multi-centre studySemantic proximity Read

MRICombo: a deep-learning-based framework for universal volume segmentation, grading-staging, and malignancy detection across heterogeneous MRISemantic proximity Read

Comparative Analysis of Deep Learning Architectures for Multi-Disease Classification of Chest X-raysSemantic proximity Read

Assessing Quantitative Performance and Expert Review of Multiple Deep Learning-Based Frameworks for Computed Tomography-based Abdominal Organ Auto-SegmentationSemantic proximity Read

3D U-Net For Segmentation of Covid-19-Associated Pulmonary Infiltrates Using Transfer Learning: State-Of-The-Art Results on Affordable HardwareSemantic proximity Read

An Optimized EfficientNet-B3 Based Lung Cancer Detection Framework with Stratified Splitting and Deployment-Ready Torch Script IntegrationSemantic proximity Read

DeepPMD: A Comprehensive Deep Learning Framework for Primary-Metastatic Classification and Origin Prediction in Lung Adenocarcinoma – Multi-Center Whole Slide Image ValidationSemantic proximity Read

Deep learning-assisted multiple organ segmentation from whole-body CT imagesSemantic proximity Read

ThyroNet-X4 Genesis: An Advanced Deep Learning Model for Auxiliary Diagnosis of Thyroid Nodules' MalignancySemantic proximity Read