在当代流行的音乐作品中，鼓声设计通常是通过繁琐的浏览和处理声音库中预录的样品的处理来执行的。人们还可以使用专门的合成硬件，通常通过低级，音乐上毫无意义的参数来控制。如今，深度学习领域提供了通过学习的高级功能来控制合成过程的方法，并允许产生各种声音。在本文中，我们提出了Drumgan VST，这是一个使用生成对抗网络合成鼓声的插件。Drumgan VST可在44.1 kHz样品速率音频上运行，提供独立且连续的仪表类控件，并具有编码的神经网络，该网络映射到GAN的潜在空间中，从而可以重新合成并操纵前持有的鼓声。我们提供了许多声音示例和建议的VST插件的演示。

Non-invasive prostate cancer detection from MRI has the potential to revolutionize patient care by providing early detection of clinically-significant disease (ISUP grade group >= 2), but has thus far shown limited positive predictive value. To address this, we present an MRI-based deep learning method for predicting clinically significant prostate cancer applicable to a patient population with subsequent ground truth biopsy results ranging from benign pathology to ISUP grade group~5. Specifically, we demonstrate that mixed supervision via diverse histopathological ground truth improves classification performance despite the cost of reduced concordance with image-based segmentation. That is, where prior approaches have utilized pathology results as ground truth derived from targeted biopsies and whole-mount prostatectomy to strongly supervise the localization of clinically significant cancer, our approach also utilizes weak supervision signals extracted from nontargeted systematic biopsies with regional localization to improve overall performance. Our key innovation is performing regression by distribution rather than simply by value, enabling use of additional pathology findings traditionally ignored by deep learning strategies. We evaluated our model on a dataset of 973 (testing n=160) multi-parametric prostate MRI exams collected at UCSF from 2015-2018 followed by MRI/ultrasound fusion (targeted) biopsy and systematic (nontargeted) biopsy of the prostate gland, demonstrating that deep networks trained with mixed supervision of histopathology can significantly exceed the performance of the Prostate Imaging-Reporting and Data System (PI-RADS) clinical standard for prostate MRI interpretation.

每当有标记的图像的大型数据集可用时，深度神经模型在图像识别任务中表现出色。放射学上最大的数据集可用于筛查乳房X线摄影。最近的报告，包括在高影响期刊中，记录了受过训练的放射科医生或以上的深层模型的性能。尚不清楚的是，这些训练有素的模型的性能是否强大并在数据集中重复。在这里，我们评估了四个公开可用的乳房X线摄影数据集上五个已发表的最先进模型的性能。公共数据集的大小有限无法重新训练该模型，因此我们只能评估那些已通过预训练参数提供的模型。在可用测试数据的地方，我们复制了已发布的结果。但是，训练有素的模型在样本外数据上的表现较差，除非基于乳房X线检查检查的所有四个标准视图。我们得出的结论是，未来的进步将取决于一致的努力，以使公开可用的更多样化和更大的乳房X线摄影数据集。同时，应谨慎判断不伴随培训的独立验证模型的结果。