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
this https URL as a part
of a global open-source initiative dedicated to accelerating advancement in
machine learning to aid clinicians in the fight against cancer.

通过引入一种解剖条件控制的潜在扩散策略，我们探索了潜在扩散对于生成逼真的前列腺 DWI 数据的有效性。经验结果表明，我们提出的 Cancer-Net PCa-Gen 策略通过可控的肿瘤位置和更好的解剖和纹理保真度提高了多样性前列腺图像的合成。这些关键特性使其非常适合增加真实患者数据，从而使神经网络能够在更多样化和全面的数据分布上进行训练。

癌症网 PCa-Gen：通过解剖条件控制的潜在扩散生成逼真的前列腺扩散加权成像数据

Cancer-Net PCa-Gen: Synthesis of Realistic Prostate Diffusion Weighted  Imaging Data via Anatomic-Conditional Controlled Latent Diffusion

The decoding of brain neural networks has been an intriguing topic in
neuroscience for a well-rounded understanding of different types of brain
disorders and cognitive stimuli. Integrating different types of connectivity,
e.g., Functional Connectivity (FC) and Structural Connectivity (SC), from
multi-modal imaging techniques can take their complementary information into
account and therefore have the potential to get better decoding capability.
However, traditional approaches for integrating FC and SC overlook the
dynamical variations, which stand a great chance to over-generalize the brain
neural network. In this paper, we propose a Joint kernel Graph Attention
Network (JGAT), which is a new multi-modal temporal graph attention network
framework. It integrates the data from functional Magnetic Resonance Images
(fMRI) and Diffusion Weighted Imaging (DWI) while preserving the dynamic
information at the same time. We conduct brain-decoding tasks with our JGAT on
four independent datasets: three of 7T fMRI datasets from the Human Connectome
Project (HCP) and one from animal neural recordings. Furthermore, with
Attention Scores (AS) and Frame Scores (FS) computed and learned from the
model, we can locate several informative temporal segments and build meaningful
dynamical pathways along the temporal domain for the HCP datasets. The URL to
the code of JGAT model: this https URL

本文提出了一种新的多模态时间图注意力网络框架 —— 联合核图注意力网络（Joint kernel Graph Attention Network，JGAT），结合了功能磁共振成像（fMRI）和扩散加权成像（DWI）的数据，可以保留动态信息，具有更好的编码能力。使用 JGAT 模型从多个 7T fMRI 数据集中对大脑进行解码任务，同时可以计算和学习到注意力得分（AS）和帧得分（FS）来定位多个信息时间段，并建立富有意义的动态通路。