Cover Image

Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Dewbury, Jarett, Tai, Chi-en Amy, Wong, Alexander
Format:	Preprint
Published:	2025
Subjects:	Image and Video Processing Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Cancer-Net PCa-MultiSeg: Multimodal Enhancement of Prostate Cancer Lesion Segmentation Using Synthetic Correlated Diffusion Imaging
by: Dewbury, Jarett, et al.
Published: (2025)

Optimizing Synthetic Correlated Diffusion Imaging for Breast Cancer Tumour Delineation
by: Tai, Chi-en Amy, et al.
Published: (2024)

Improving Breast Cancer Grade Prediction with Multiparametric MRI Created Using Optimized Synthetic Correlated Diffusion Imaging
by: Tai, Chi-en Amy, et al.
Published: (2024)

Using Multiparametric MRI with Optimized Synthetic Correlated Diffusion Imaging to Enhance Breast Cancer Pathologic Complete Response Prediction
by: Tai, Chi-en Amy, et al.
Published: (2024)

Enhancing Clinically Significant Prostate Cancer Prediction in T2-weighted Images through Transfer Learning from Breast Cancer
by: Tai, Chi-en Amy, et al.
Published: (2024)

Double-Condensing Attention Condenser: Leveraging Attention in Deep Learning to Detect Skin Cancer from Skin Lesion Images
by: Tai, Chi-en Amy, et al.
Published: (2023)

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

MicroSegNet: A Deep Learning Approach for Prostate Segmentation on Micro-Ultrasound Images
by: Jiang, Hongxu, et al.
Published: (2023)

Optimizing Federated Learning Configurations for MRI Prostate Segmentation and Cancer Detection: A Simulation Study
by: Moradi, Ashkan, et al.
Published: (2025)

Using Diffusion Models to Generate Synthetic Labelled Data for Medical Image Segmentation
by: Saragih, Daniel, et al.
Published: (2023)

SegX: Improving Interpretability of Clinical Image Diagnosis with Segmentation-based Enhancement
by: Zhang, Yuhao, et al.
Published: (2025)

Gland Segmentation Using SAM With Cancer Grade as a Prompt
by: Zhu, Yijie, et al.
Published: (2025)

Phi-SegNet: Phase-Integrated Supervision for Medical Image Segmentation
by: Ali, Shams Nafisa, et al.
Published: (2026)

GUSL: A Novel and Efficient Machine Learning Model for Prostate Segmentation on MRI
by: Yang, Jiaxin, et al.
Published: (2025)

Registration-Enhanced Segmentation Method for Prostate Cancer in Ultrasound Images
by: Sang, Shengtian, et al.
Published: (2025)

Do High-Performance Image-to-Image Translation Networks Enable the Discovery of Radiomic Features? Application to MRI Synthesis from Ultrasound in Prostate Cancer
by: Salmanpour, Mohammad R., et al.
Published: (2024)

Classification of Gleason Grading in Prostate Cancer Histopathology Images Using Deep Learning Techniques: YOLO, Vision Transformers, and Vision Mamba
by: Malekmohammadi, Amin, et al.
Published: (2024)

SegRet: An Efficient Design for Semantic Segmentation with Retentive Network
by: Li, Zhiyuan, et al.
Published: (2025)

UNIR-Net: A Novel Approach for Restoring Underwater Images with Non-Uniform Illumination Using Synthetic Data
by: Perez-Zarate, Ezequiel, et al.
Published: (2025)

T2-Only Prostate Cancer Prediction by Meta-Learning from Bi-Parametric MR Imaging
by: Yi, Weixi, et al.
Published: (2024)

Ultra-Strong Gradient Diffusion MRI with Self-Supervised Learning for Prostate Cancer Characterization
by: Patil, Tanishq, et al.
Published: (2025)

PGDiffSeg: Prior-Guided Denoising Diffusion Model with Parameter-Shared Attention for Breast Cancer Segmentation
by: Feng, Feiyan, et al.
Published: (2024)

RadHop-Net: A Lightweight Radiomics-to-Error Regression for False Positive Reduction In MRI Prostate Cancer Detection
by: Magoulianitis, Vasileios, et al.
Published: (2025)

TRUSWorthy: Toward Clinically Applicable Deep Learning for Confident Detection of Prostate Cancer in Micro-Ultrasound
by: Harmanani, Mohamed, et al.
Published: (2025)

Digital Volumetric Biopsy Cores Improve Gleason Grading of Prostate Cancer Using Deep Learning
by: Redekop, Ekaterina, et al.
Published: (2024)

Hierarchical SegNet with Channel and Context Attention for Accurate Lung Segmentation in Chest X-ray Images
by: Khaniki, Mohammad Ali Labbaf, et al.
Published: (2024)

CATFA-Net: A Trans-Convolutional Approach for Accurate Medical Image Segmentation
by: Mallick, Siddhartha, et al.
Published: (2026)

Adaptive Voxel-Weighted Loss Using L1 Norms in Deep Neural Networks for Detection and Segmentation of Prostate Cancer Lesions in PET/CT Images
by: Dzikunu, Obed Korshie, et al.
Published: (2025)

Deep Learning-Driven Quantitative Spectroscopic Photoacoustic Imaging for Segmentation and Oxygen Saturation Estimation
by: Shang, Ruibo, et al.
Published: (2025)

KongNet: A Multi-headed Deep Learning Model for Detection and Classification of Nuclei in Histopathology Images
by: Lv, Jiaqi, et al.
Published: (2025)

Domain Transfer Through Image-to-Image Translation for Uncertainty-Aware Prostate Cancer Classification
by: Zhou, Meng, et al.
Published: (2023)

Anatomically and Metabolically Informed Diffusion for Unified Denoising and Segmentation in Low-Count PET Imaging
by: Xia, Menghua, et al.
Published: (2025)

HemSeg-200: A Voxel-Annotated Dataset for Intracerebral Hemorrhages Segmentation in Brain CT Scans
by: Song, Changwei, et al.
Published: (2024)

SmoothSegNet: A Global-Local Framework for Liver Tumor Segmentation with Clinical KnowledgeInformed Label Smoothing
by: Wang, Hairong, et al.
Published: (2024)

DM-SegNet: Dual-Mamba Architecture for 3D Medical Image Segmentation with Global Context Modeling
by: Ji, Hangyu
Published: (2025)

MedSegNet10: A Publicly Accessible Network Repository for Split Federated Medical Image Segmentation
by: Shiranthika, Chamani, et al.
Published: (2025)

Deep Learning-Based Desikan-Killiany Parcellation of the Brain Using Diffusion MRI
by: Sadegheih, Yousef, et al.
Published: (2025)

DeepBayesFlow: A Bayesian Structured Variational Framework for Generalizable Prostate Segmentation via Expressive Posteriors and SDE-Girsanov Uncertainty Modeling
by: Fang, Zhuoyi
Published: (2026)

Deep Learning Enabled Segmentation, Classification and Risk Assessment of Cervical Cancer
by: Shaik, Abdul Samad, et al.
Published: (2025)

Benchmarking Image Transformers for Prostate Cancer Detection from Ultrasound Data
by: Harmanani, Mohamed, et al.
Published: (2024)