The PI-CAI (Prostate Imaging: Cancer AI) challenge led to expert-level
diagnostic algorithms for clinically significant prostate cancer detection. The
algorithms receive biparametric MRI scans as input, which consist of
T2-weighted and diffusion-weighted scans. These scans can be misaligned due to
multiple factors in the scanning process. Image registration can alleviate this
issue by predicting the deformation between the sequences. We investigate the
effect of image registration on the diagnostic performance of AI-based prostate
cancer diagnosis. First, the image registration algorithm, developed in
MeVisLab, is analyzed using a dataset with paired lesion annotations. Second,
the effect on diagnosis is evaluated by comparing case-level cancer diagnosis
performance between using the original dataset, rigidly aligned
diffusion-weighted scans, or deformably aligned diffusion-weighted scans. Rigid
registration showed no improvement. Deformable registration demonstrated a
substantial improvement in lesion overlap (+10% median Dice score) and a
positive yet non-significant improvement in diagnostic performance (+0.3%
AUROC, p=0.18). Our investigation shows that a substantial improvement in
lesion alignment does not directly lead to a significant improvement in
diagnostic performance. Qualitative analysis indicated that jointly developing
image registration methods and diagnostic AI algorithms could enhance
diagnostic accuracy and patient outcomes.

通过使用图像配准算法研究了人工智能诊断算法的影响，发现虽然图像配准显著提高了病变对齐效果，但直接导致诊断准确性的显著提升仍未能得到证实。定性分析表明，同时开发图像配准方法和诊断人工智能算法可以提高诊断准确性和患者预后。

基于变形 MRI 序列配准的人工智能前列腺癌诊断

Deformable MRI Sequence Registration for AI-based Prostate Cancer  Diagnosis

Electrocardiogram (ECG) monitoring is one of the most powerful technique of
cardiovascular disease (CVD) early identification, and the introduction of
intelligent wearable ECG devices has enabled daily monitoring. However, due to
the need for professional expertise in the ECGs interpretation, general public
access has once again been restricted, prompting the need for the development
of advanced diagnostic algorithms. Classic rule-based algorithms are now
completely outperformed by deep learning based methods. But the advancement of
smart diagnostic algorithms is hampered by issues like small dataset,
inconsistent data labeling, inefficient use of local and global ECG
information, memory and inference time consuming deployment of multiple models,
and lack of information transfer between tasks. We propose a multi-resolution
model that can sustain high-resolution low-level semantic information
throughout, with the help of the development of low-resolution high-level
semantic information, by capitalizing on both local morphological information
and global rhythm information. From the perspective of effective data leverage
and inter-task knowledge transfer, we develop a parameter isolation based ECG
continual learning (ECG-CL) approach. We evaluated our model's performance on
four open-access datasets by designing segmentation-to-classification for
cross-domain incremental learning, minority-to-majority class for category
incremental learning, and small-to-large sample for task incremental learning.
Our approach is shown to successfully extract informative morphological and
rhythmic features from ECG segmentation, leading to higher quality
classification results. From the perspective of intelligent wearable
applications, the possibility of a comprehensive ECG interpretation algorithm
based on single-lead ECGs is also confirmed.

提出了一个多分辨率模型，利用局部形态信息和全局节奏信息，通过开发低分辨率高级语义信息，持续维持高分辨率低级语义信息，从而成功地从 ECG 细分中提取信息丰富的形态和节律特征，从智能可穿戴应用的角度，也确认基于单导联 ECG 的全面 ECG 解释算法的可能性。

ECG-CL: 一种基于持续学习的全面心电图解释方法

ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on  Continual Learning

While it's always possible to compute a variational approximation to a
posterior distribution, it can be difficult to discover problems with this
approximation. We propose two diagnostic algorithms to alleviate this problem.
The Pareto-smoothed importance sampling (PSIS) diagnostic gives a goodness of
fit measurement for joint distributions, while simultaneously improving the
error in the estimate. The variational simulation-based calibration (VSBC)
assesses the average performance of point estimates.

该研究提出了两种诊断算法，分别是 Pareto-smoothed importance sampling (PSIS) 诊断和 variational simulation-based calibration (VSBC) 评估，用于评估变分近似推断的正确性并改进误差。