In 2020, prostate cancer saw a staggering 1.4 million new cases, resulting in
over 375,000 deaths. The accurate identification of clinically significant
prostate cancer is crucial for delivering effective treatment to patients.
Consequently, there has been a surge in research exploring the application of
deep neural networks to predict clinical significance based on magnetic
resonance images. However, these networks demand extensive datasets to attain
optimal performance. Recently, transfer learning emerged as a technique that
leverages acquired features from a domain with richer data to enhance the
performance of a domain with limited data. In this paper, we investigate the
improvement of clinically significant prostate cancer prediction in T2-weighted
images through transfer learning from breast cancer. The results demonstrate a
remarkable improvement of over 30% in leave-one-out cross-validation accuracy.

通过从乳腺癌中进行迁移学习，我们研究了在 T2 加权图像中改善临床意义前列腺癌预测的方法，结果显示在 leave-one-out 交叉验证准确性中取得了超过 30% 的显著提升。

基于乳腺癌迁移学习的 T2 加权图像对临床相关性前列腺癌预测的增强

Enhancing Clinically Significant Prostate Cancer Prediction in  T2-weighted Images through Transfer Learning from Breast Cancer

Protein Language Models (PLMs) have emerged as performant and scalable tools
for predicting the functional impact and clinical significance of
protein-coding variants, but they still lag experimental accuracy. Here, we
present a novel fine-tuning approach to improve the performance of PLMs with
experimental maps of variant effects from Deep Mutational Scanning (DMS) assays
using a Normalised Log-odds Ratio (NLR) head. We find consistent improvements
in a held-out protein test set, and on independent DMS and clinical variant
annotation benchmarks from ProteinGym and ClinVar. These findings demonstrate
that DMS is a promising source of sequence diversity and supervised training
data for improving the performance of PLMs for variant effect prediction.

通过深度突变扫描（DMS）试验的实验性映射，使用归一化的对数比率（NLR）头部，提出了一种改进蛋白质语言模型（PLMs）性能的微调方法，以改善变异效应预测的性能。

用深入突变扫描对蛋白质语言模型进行微调，提高变体效应预测

Fine-tuning Protein Language Models with Deep Mutational Scanning  improves Variant Effect Prediction

Multiparametric magnetic resonance imaging (mpMRI) has demonstrated promising
results in prostate cancer (PCa) detection using deep convolutional neural
networks (CNNs). Recently, transformers have achieved competitive performance
compared to CNNs in computer vision. Large-scale transformers need abundant
annotated data for training, which are difficult to obtain in medical imaging.
Self-supervised learning can effectively leverage unlabeled data to extract
useful semantic representations without annotation and its associated costs.
This can improve model performance on downstream tasks with limited labelled
data and increase generalizability. We introduce a novel end-to-end
Cross-Shaped windows (CSwin) transformer UNet model, CSwin UNet, to detect
clinically significant prostate cancer (csPCa) in prostate bi-parametric MR
imaging (bpMRI) and demonstrate the effectiveness of our proposed
self-supervised pre-training framework. Using a large prostate bpMRI dataset
with 1500 patients, we first pre-train CSwin transformer using multi-task
self-supervised learning to improve data-efficiency and network
generalizability. We then finetuned using lesion annotations to perform csPCa
detection. Five-fold cross validation shows that self-supervised CSwin UNet
achieves 0.888 AUC and 0.545 Average Precision (AP), significantly
outperforming four state-of-the-art models (Swin UNETR, DynUNet, Attention
UNet, UNet). Using a separate bpMRI dataset with 158 patients, we evaluated our
model robustness to external hold-out data. Self-supervised CSwin UNet achieves
0.79 AUC and 0.45 AP, still outperforming all other comparable methods and
demonstrating generalization to a dataset shift.

本研究通过引入自监督预训练框架，提出了一种全新的端到端 Cross-Shaped windows (CSwin) transformer UNet 模型，用于检测临床重要的前列腺癌症，通过在大量未标记的数据上进行学习，提高数据效率和网络的泛化能力，成功地提高了模型性能。