Cancer-Net PCa-Gen: Synthesis of Realistic Prostate Diffusion Weighted
Imaging Data via Anatomic-Conditional Controlled Latent Diffusion
- URL: http://arxiv.org/abs/2311.18612v1
- Date: Thu, 30 Nov 2023 15:11:03 GMT
- Title: Cancer-Net PCa-Gen: Synthesis of Realistic Prostate Diffusion Weighted
Imaging Data via Anatomic-Conditional Controlled Latent Diffusion
- Authors: Aditya Sridhar and Chi-en Amy Tai and Hayden Gunraj and Yuhao Chen and
Alexander Wong
- Abstract summary: In Canada, prostate cancer is the most common form of cancer in men and accounted for 20% of new cancer cases for this demographic in 2022.
There has been significant interest in the development of deep neural networks for prostate cancer diagnosis, prognosis, and treatment planning using diffusion weighted imaging (DWI) data.
In this study, we explore the efficacy of latent diffusion for generating realistic prostate DWI data through the introduction of an anatomic-conditional controlled latent diffusion strategy.
- Score: 68.45407109385306
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In Canada, prostate cancer is the most common form of cancer in men and
accounted for 20% of new cancer cases for this demographic in 2022. Due to
recent successes in leveraging machine learning for clinical decision support,
there has been significant interest in the development of deep neural networks
for prostate cancer diagnosis, prognosis, and treatment planning using
diffusion weighted imaging (DWI) data. A major challenge hindering widespread
adoption in clinical use is poor generalization of such networks due to
scarcity of large-scale, diverse, balanced prostate imaging datasets for
training such networks. In this study, we explore the efficacy of latent
diffusion for generating realistic prostate DWI data through the introduction
of an anatomic-conditional controlled latent diffusion strategy. To the best of
the authors' knowledge, this is the first study to leverage conditioning for
synthesis of prostate cancer imaging. Experimental results show that the
proposed strategy, which we call Cancer-Net PCa-Gen, enhances synthesis of
diverse prostate images through controllable tumour locations and better
anatomical and textural fidelity. These crucial features make it well-suited
for augmenting real patient data, enabling neural networks to be trained on a
more diverse and comprehensive data distribution. The Cancer-Net PCa-Gen
framework and sample images have been made publicly available at
https://www.kaggle.com/datasets/deetsadi/cancer-net-pca-gen-dataset as a part
of a global open-source initiative dedicated to accelerating advancement in
machine learning to aid clinicians in the fight against cancer.
Related papers
- Improving Breast Cancer Grade Prediction with Multiparametric MRI Created Using Optimized Synthetic Correlated Diffusion Imaging [71.91773485443125]
Grading plays a vital role in breast cancer treatment planning.
The current tumor grading method involves extracting tissue from patients, leading to stress, discomfort, and high medical costs.
This paper examines using optimized CDI$s$ to improve breast cancer grade prediction.
arXiv Detail & Related papers (2024-05-13T15:48:26Z) - Survival Prediction Across Diverse Cancer Types Using Neural Networks [40.392772795903795]
Gastric cancer and Colon adenocarcinoma represent widespread and challenging malignancies.
Medical community has embraced the 5-year survival rate as a vital metric for estimating patient outcomes.
This study introduces a pioneering approach to enhance survival prediction models for gastric and Colon adenocarcinoma patients.
arXiv Detail & Related papers (2024-04-11T21:47:13Z) - Double-Condensing Attention Condenser: Leveraging Attention in Deep Learning to Detect Skin Cancer from Skin Lesion Images [61.36288157482697]
Skin cancer is the most common type of cancer in the United States and is estimated to affect one in five Americans.
Recent advances have demonstrated strong performance on skin cancer detection, as exemplified by state of the art performance in the SIIM-ISIC Melanoma Classification Challenge.
This paper explores leveraging an efficient self-attention structure to detect skin cancer in skin lesion images and introduces a deep neural network design with DC-AC customized for skin cancer detection from skin lesion images.
arXiv Detail & Related papers (2023-11-20T10:45:39Z) - Cancer-Net PCa-Data: An Open-Source Benchmark Dataset for Prostate
Cancer Clinical Decision Support using Synthetic Correlated Diffusion Imaging
Data [75.77035221531261]
Cancer-Net PCa-Data is an open-source benchmark dataset of volumetric CDI$s$ imaging data of PCa patients.
Cancer-Net PCa-Data is the first-ever public dataset of CDI$s$ imaging data for PCa.
arXiv Detail & Related papers (2023-11-20T10:28:52Z) - ANNCRIPS: Artificial Neural Networks for Cancer Research In Prediction &
Survival [0.0]
This study focuses on the development and validation of an intelligent mathematical model utilizing Artificial Neural Networks (ANNs)
The model's implementation demonstrates promising potential in reducing the incidence of false positives, thereby improving patient outcomes.
The long-term goal is to make this solution readily available for deployment in various screening centers, hospitals, and research institutions.
arXiv Detail & Related papers (2023-09-26T08:11:35Z) - A Multi-Institutional Open-Source Benchmark Dataset for Breast Cancer
Clinical Decision Support using Synthetic Correlated Diffusion Imaging Data [82.74877848011798]
Cancer-Net BCa is a multi-institutional open-source benchmark dataset of volumetric CDI$s$ imaging data of breast cancer patients.
Cancer-Net BCa is publicly available as a part of a global open-source initiative dedicated to accelerating advancement in machine learning to aid clinicians in the fight against cancer.
arXiv Detail & Related papers (2023-04-12T05:41:44Z) - Implementation of Convolutional Neural Network Architecture on 3D
Multiparametric Magnetic Resonance Imaging for Prostate Cancer Diagnosis [0.0]
We propose a novel deep learning approach for automatic classification of prostate lesions in magnetic resonance images.
Our framework achieved the classification performance with the area under a Receiver Operating Characteristic curve value of 0.87.
Our proposed framework reflects the potential of assisting medical image interpretation in prostate cancer and reducing unnecessary biopsies.
arXiv Detail & Related papers (2021-12-29T16:47:52Z) - Gleason Grading of Histology Prostate Images through Semantic
Segmentation via Residual U-Net [60.145440290349796]
The final diagnosis of prostate cancer is based on the visual detection of Gleason patterns in prostate biopsy by pathologists.
Computer-aided-diagnosis systems allow to delineate and classify the cancerous patterns in the tissue.
The methodological core of this work is a U-Net convolutional neural network for image segmentation modified with residual blocks able to segment cancerous tissue.
arXiv Detail & Related papers (2020-05-22T19:49:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.