Imagen de Portada

DECT-based Space-Squeeze Method for Multi-Class Classification of Metastatic Lymph Nodes in Breast Cancer

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Jiang, Hai, Zheng, Chushan, Pan, Jiawei, Zhou, Yuanpin, Liu, Qiongting, Zhang, Xiang, Shen, Jun, Lu, Yao
Formato:	Preprint
Publicado:	2025
Materias:	Image and Video Processing Computer Vision and Pattern Recognition
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Anatomy-Guided Multitask Learning for MRI-Based Classification of Placenta Accreta Spectrum and its Subtypes
por: Jiang, Hai, et al.
Publicado: (2025)

Towards a Comprehensive Benchmark for Pathological Lymph Node Metastasis in Breast Cancer Sections
por: Ling, Xitong, et al.
Publicado: (2024)

WeGA: Weakly-Supervised Global-Local Affinity Learning Framework for Lymph Node Metastasis Prediction in Rectal Cancer
por: Gao, Yifan, et al.
Publicado: (2025)

LRMR: LLM-Driven Relational Multi-node Ranking for Lymph Node Metastasis Assessment in Rectal Cancer
por: Dong, Yaoxian, et al.
Publicado: (2025)

Fine-tuned Transformer Models for Breast Cancer Detection and Classification
por: Osman, Showkat, et al.
Publicado: (2025)

Multi-modal Learning with Missing Modality in Predicting Axillary Lymph Node Metastasis
por: Zhang, Shichuan, et al.
Publicado: (2024)

Segmentation of Mediastinal Lymph Nodes in CT with Anatomical Priors
por: Mathai, Tejas Sudharshan, et al.
Publicado: (2024)

MMFusion: Multi-modality Diffusion Model for Lymph Node Metastasis Diagnosis in Esophageal Cancer
por: Wu, Chengyu, et al.
Publicado: (2024)

Universal Lymph Node Detection in Multiparametric MRI with Selective Augmentation
por: Mathai, Tejas Sudharshan, et al.
Publicado: (2025)

CT Synthesis with Conditional Diffusion Models for Abdominal Lymph Node Segmentation
por: Yu, Yongrui, et al.
Publicado: (2024)

LN-Gen: Rectal Lymph Nodes Generation via Anatomical Features
por: Guo, Weidong, et al.
Publicado: (2024)

The Application of Deep Learning for Lymph Node Segmentation: A Systematic Review
por: Qu, Jingguo, et al.
Publicado: (2025)

Pathology-Informed Latent Diffusion Model for Anomaly Detection in Lymph Node Metastasis
por: Wang, Jiamu, et al.
Publicado: (2025)

Breast Cancer Image Classification Method Based on Deep Transfer Learning
por: Wang, Weimin, et al.
Publicado: (2024)

CAFusion: Controllable Anatomical Synthesis of Perirectal Lymph Nodes via SDF-guided Diffusion
por: Guo, Weidong, et al.
Publicado: (2025)

Attention-Map Augmentation for Hypercomplex Breast Cancer Classification
por: Lopez, Eleonora, et al.
Publicado: (2023)

FECT: Classification of Breast Cancer Pathological Images Based on Fusion Features
por: Hao, Jiacheng, et al.
Publicado: (2025)

Evaluating Deep Learning Models for Breast Cancer Classification: A Comparative Study
por: Eskandari, Sania, et al.
Publicado: (2024)

Adversarially Robust Feature Learning for Breast Cancer Diagnosis
por: Hao, Degan, et al.
Publicado: (2024)

Predicting Lung Cancer's Metastats' Locations Using Bioclinical Model
por: Lazebnik, Teddy, et al.
Publicado: (2023)

Performance Evaluation of Deep Learning and Transformer Models Using Multimodal Data for Breast Cancer Classification
por: Hussain, Sadam, et al.
Publicado: (2024)

SegRap2025: A Benchmark of Gross Tumor Volume and Lymph Node Clinical Target Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma
por: Fu, Jia, et al.
Publicado: (2026)

Breast Cancer Classification in Deep Ultraviolet Fluorescence Images Using a Patch-Level Vision Transformer Framework
por: Afshin, Pouya, et al.
Publicado: (2025)

Classification of Breast Tumours Based on Histopathology Images Using Deep Features and Ensemble of Gradient Boosting Methods
por: Abbasniya, Mohammad Reza, et al.
Publicado: (2022)

M$^4$oE: A Foundation Model for Medical Multimodal Image Segmentation with Mixture of Experts
por: Jiang, Yufeng, et al.
Publicado: (2024)

Hybrid Deep Learning and Handcrafted Feature Fusion for Mammographic Breast Cancer Classification
por: Tschuchnig, Maximilian, et al.
Publicado: (2025)

MSFMamba: Multi-Scale Feature Fusion State Space Model for Multi-Source Remote Sensing Image Classification
por: Gao, Feng, et al.
Publicado: (2024)

PersonalizedUS: Interpretable Breast Cancer Risk Assessment with Local Coverage Uncertainty Quantification
por: Fröhlich, Alek, et al.
Publicado: (2024)

An Artificial Intelligence Model for Early Stage Breast Cancer Detection from Biopsy Images
por: Chaudhary, Neil, et al.
Publicado: (2025)

Subgroup Performance of a Commercial Digital Breast Tomosynthesis Model for Breast Cancer Detection
por: Brown-Mulry, Beatrice, et al.
Publicado: (2025)

PathoHR: Breast Cancer Survival Prediction on High-Resolution Pathological Images
por: Luo, Yang, et al.
Publicado: (2025)

Control Copy-Paste: Controllable Diffusion-Based Augmentation Method for Remote Sensing Few-Shot Object Detection
por: Liu, Yanxing, et al.
Publicado: (2025)

Efficient Breast and Ovarian Cancer Classification via ViT-Based Preprocessing and Transfer Learning
por: Rawat, Richa, et al.
Publicado: (2025)

Breast Cancer Classification with Enhanced Interpretability: DALAResNet50 and DT Grad-CAM
por: Liu, Suxing
Publicado: (2023)

A Calibration Method for Indirect Time-of-Flight Cameras to Eliminate Internal Scattering Interference
por: Du, Yansong, et al.
Publicado: (2025)

Small Drone Classification with Light CNN and New Micro-Doppler Signature Extraction Method Based on A-SPC Technique
por: Park, Junhyeong, et al.
Publicado: (2020)

Multi-Beholder: Biomarker Prediction for Low-Grade Glioma with Multiple Instance Learning and One-Class Classification
por: Fang, Zijie, et al.
Publicado: (2023)

PCa-RadHop: A Transparent and Lightweight Feed-forward Method for Clinically Significant Prostate Cancer Segmentation
por: Magoulianitis, Vasileios, et al.
Publicado: (2024)

Federated Breast Cancer Detection Enhanced by Synthetic Ultrasound Image Augmentation
por: Pan, Hongyi, et al.
Publicado: (2025)

Cuboid-Net: A Multi-Branch Convolutional Neural Network for Joint Space-Time Video Super Resolution
por: Fu, Congrui, et al.
Publicado: (2024)