The prediction of pancreatic ductal adenocarcinoma therapy response is a clinically challenging and important task in this high-mortality tumour entity. The training of neural networks able to tackle this challenge is impeded by a lack of large datasets and the difficult anatomical localisation of the pancreas. Here, we propose a hybrid deep neural network pipeline to predict tumour response to initial chemotherapy which is based on the Response Evaluation Criteria in Solid Tumors (RECIST) score, a standardised method for cancer response evaluation by clinicians as well as tumour markers, and clinical evaluation of the patients. We leverage a combination of representation transfer from segmentation to classification, as well as localisation and representation learning. Our approach yields a remarkably data-efficient method able to predict treatment response with a ROC-AUC of 63.7% using only 477 datasets in total.

使用差异隐私（DP）等正式隐私技术的机器学习使人们可以从敏感的医学成像数据中获得有价值的见解，同时有望保护患者隐私，但通常具有急剧的隐私 - 实用性权衡。在这项工作中，我们建议使用DP使用可进入的卷积网络进行医学图像分析。它们提高的功能质量和参数效率可获得明显的准确性，从而缩小了隐私 - 实用性差距。

无监督的异常检测已成为一种流行的方法，可以检测医学图像中的病理，因为它不需要监督或标签进行训练。最常见的是，异常检测模型会生成输入映像的“正常”版本，而Pixel $ l^p $ - 两者的差异用于本地化异常。但是，大多数医学图像中存在的复杂解剖结构的不完善重建通常是由于不完善的重建而发生的。该方法还无法检测到没有与周围组织的强度差异很大的异常。我们建议使用特征映射功能解决此问题，该功能将输入强度图像转换为具有多个通道的空间，在该空间中可以沿着从原始图像提取的不同判别特征地图检测到异常。然后，我们使用结构相似性损失在该空间中训练自动编码器模型，该模型不仅考虑强度差异，而且考虑对比度和结构。我们的方法大大提高了大脑MRI的两个医学数据集的性能。代码和实验可从https://github.com/felime/feature-autoencoder获得

在用于医学图像分析的联合学习中，学习方案的安全至关重要。这种设置通常会被针对联邦使用的私人数据或模型本身完整性的对手所损害。这要求医学成像社区开发机制，以训练私人和强大的对抗数据的协作模型。为了应对这些挑战，我们提出了一个实用的开源框架，以研究结合差异隐私，模型压缩和对抗性训练的有效性，以提高模型在火车和推理时间攻击下针对对抗性样本的鲁棒性。使用我们的框架，我们实现了竞争性模型的性能，模型的大小显着降低以及改进的经验对抗性鲁棒性，而无需严重的性能降解，对医学图像分析至关重要。

需要在规模上利用大型和多样化数据集进行机器学习（ML），以促进对许多有意义的问题的科学洞察力。但是，由于数据治理规定，如GDPR以及道德问题，个人和敏感数据的聚合是有问题的，这促使开发替代策略，如分布式ML（DML）。诸如联邦学习（FL）之类的技术允许数据所有者维护数据治理并在本地执行模型培训，而无需共享其数据。 FL和相关技术通常被描述为隐私保留。我们解释了为什么这个术语不合适，并概述与过度依赖的议定书相关的风险，这些风险在没有考虑到隐私的正式定义。我们进一步提供了关于这些算法如何增强的建议和示例，以提供一般ML受众的治理，安全，隐私和可验证的保证，而无需前往正式隐私技巧。

已经证明对比学习有效地对未标记数据的预训练图像模型有效，并且有希望的医学图像分类等任务的结果。在预训练期间使用配对文本和图像（例如放射性报告和图像）甚至进一步改善了结果。尽管如此，大多数现有方法将图像分类为下游任务，并且对于像语义分割或物体检测等本地化任务可能不是最佳的。因此，我们提出了从愿景和文本（Lovt）的局部代表学习，以实现我们最佳知识，这是针对本地化医学成像任务的第一种文本监督的预训练方法。我们的方法将实例级图像报告对比学习与图像区域和报告句子表示的局部对比学习结合起来。我们评估LOVT和常用的预培训方法，这些评估框架是由五个公共数据集的胸部X光上的18个本地化任务组成的新评估框架。虽然没有单一的最佳方法，但是，在18个研究的任务中，Lovt在11个中最佳地表现出优选的选择本地化任务的首选方法。

差分隐私（DP）允许在个人数据的数据进行算法处理（如机器学习）以及提供客观隐私保障时量化隐私损失。然而，虽然诸如单独的R \'ENYI DP（RDP）的技术允许粒度，但每人隐私会计，但很少有效地调查了每个输入特征对个人隐私损失的影响。在这里，我们通过介绍一个新的概念来延长各个RDP的视图，我们称之为偏见敏感性，它利用符号自动差异来确定每个输入特征对函数梯度范数的影响。我们通过实验评估我们对私有数据库的查询的方法，在那里我们获得了对个人DP保证的私有属性的特征级别贡献。此外，我们通过研究图像分类任务上的输入像素的部分敏感性来探讨我们的神经网络培训的研究结果。

View-dependent effects such as reflections pose a substantial challenge for image-based and neural rendering algorithms. Above all, curved reflectors are particularly hard, as they lead to highly non-linear reflection flows as the camera moves. We introduce a new point-based representation to compute Neural Point Catacaustics allowing novel-view synthesis of scenes with curved reflectors, from a set of casually-captured input photos. At the core of our method is a neural warp field that models catacaustic trajectories of reflections, so complex specular effects can be rendered using efficient point splatting in conjunction with a neural renderer. One of our key contributions is the explicit representation of reflections with a reflection point cloud which is displaced by the neural warp field, and a primary point cloud which is optimized to represent the rest of the scene. After a short manual annotation step, our approach allows interactive high-quality renderings of novel views with accurate reflection flow. Additionally, the explicit representation of reflection flow supports several forms of scene manipulation in captured scenes, such as reflection editing, cloning of specular objects, reflection tracking across views, and comfortable stereo viewing. We provide the source code and other supplemental material on https://repo-sam.inria.fr/ fungraph/neural_catacaustics/

Modern speech recognition systems exhibits rapid performance degradation under domain shift. This issue is especially prevalent in data-scarce settings, such as low-resource languages, where diversity of training data is limited. In this work we propose M2DS2, a simple and sample-efficient finetuning strategy for large pretrained speech models, based on mixed source and target domain self-supervision. We find that including source domain self-supervision stabilizes training and avoids mode collapse of the latent representations. For evaluation, we collect HParl, a $120$ hour speech corpus for Greek, consisting of plenary sessions in the Greek Parliament. We merge HParl with two popular Greek corpora to create GREC-MD, a test-bed for multi-domain evaluation of Greek ASR systems. In our experiments we find that, while other Unsupervised Domain Adaptation baselines fail in this resource-constrained environment, M2DS2 yields significant improvements for cross-domain adaptation, even when a only a few hours of in-domain audio are available. When we relax the problem in a weakly supervised setting, we find that independent adaptation for audio using M2DS2 and language using simple LM augmentation techniques is particularly effective, yielding word error rates comparable to the fully supervised baselines.

In this work, we propose a novel framework for estimating the dimension of the data manifold using a trained diffusion model. A trained diffusion model approximates the gradient of the log density of a noise-corrupted version of the target distribution for varying levels of corruption. If the data concentrates around a manifold embedded in the high-dimensional ambient space, then as the level of corruption decreases, the score function points towards the manifold, as this direction becomes the direction of maximum likelihood increase. Therefore, for small levels of corruption, the diffusion model provides us with access to an approximation of the normal bundle of the data manifold. This allows us to estimate the dimension of the tangent space, thus, the intrinsic dimension of the data manifold. Our method outperforms linear methods for dimensionality detection such as PPCA in controlled experiments.