已经开发了用于预测结直肠癌（CRC）在内的临床相关生物标志物（包括微卫星不稳定性（MSI））的人工智能（AI）模型。但是，当前的深度学习网络是渴望数据的，需要大型培训数据集，这些数据集通常缺乏医疗领域。在这项研究中，基于最新的层次视觉变压器使用移位窗口（SWIN-T），我们开发了CRC中生物标志物的有效工作流程（MSI，超突击，染色体不稳定性，CPG岛甲基表型，BRAF和TP53突变）需要相对较小的数据集，但实现了最新的（SOTA）预测性能。我们的SWIN-T工作流不仅在使用TCGA-CRC-DX数据集（n = 462）的研究内交叉验证实验中大大优于已发表的模型（n = 462），而且在跨研究的外部验证中表现出极好的普遍性，并提供了SOTA AUROC使用MCO数据集进行训练（n = 1065）和相同的TCGA-CRC-DX进行测试。 Echle及其同事在同一测试数据集上使用8000个培训样本（RESNET18）实现了类似的性能（AUROC = 0.91）。 Swin-T使用小型训练数据集非常有效，并且仅使用200-500个培训样本展示出强大的预测性能。这些数据表明，Swin-T的效率可能是基于RESNET18和Shufflenet的MSI当前最新算法的效率5-10倍。此外，SWIN-T模型显示出有望作为MSI状态和BRAF突变状态的预筛查测试，可以在级联的诊断工作流程中排除和减少样品，以允许降低周转时间和节省成本。

已经开发了几种深度学习算法，以使用整个幻灯片图像（WSIS）预测癌症患者的存活。但是，WSI中与患者的生存和疾病进展有关的WSI中的图像表型对临床医生而言都是困难的，以及深度学习算法。用于生存预测的大多数基于深度学习的多个实例学习（MIL）算法使用顶级实例（例如Maxpooling）或顶级/底部实例（例如，Mesonet）来识别图像表型。在这项研究中，我们假设WSI中斑块得分分布的全面信息可以更好地预测癌症的生存。我们开发了一种基于分布的多构度生存学习算法（DeepDismisl）来验证这一假设。我们使用两个大型国际大型癌症WSIS数据集设计和执行实验-MCO CRC和TCGA Coad -Read。我们的结果表明，有关WSI贴片分数的分布的信息越多，预测性能越好。包括每个选定分配位置（例如百分位数）周围的多个邻域实例可以进一步改善预测。与最近发表的最新算法相比，DeepDismisl具有优越的预测能力。此外，我们的算法是可以解释的，可以帮助理解癌症形态表型与癌症生存风险之间的关系。

最早的早期结肠直肠癌（CRC）患者可以单独通过手术治愈，只有某些高风险的早期CRC患者受益于佐剂化学疗法。然而，很少有验证的生物标志物可用于准确预测术后化疗的生存效果。我们开发了一种新的深度学习算法（CRCNET），使用来自分子和细胞肿瘤（MCO）的全滑动图像来预测II / III CRC中辅助化疗的存活效益。我们通过交叉验证和外部使用来自癌症基因组Atlas（TCGA）的独立队列的外部验证了CRCNet。我们表明，CRCNet不仅可以准确地预测生存预后，还可以进行佐剂化疗的治疗效果。 CRCNET鉴定了来自佐剂化疗的高危亚组益处，在化疗治疗的患者中，观察到辅助化疗最大而显着的存活率。相反，在CRCNET低和中风险亚组中观察到最小化疗益处。因此，CRCNET可能在阶段II / III CRC的指导治疗方面具有很大的用途。

Are extralinguistic signals such as image pixels crucial for inducing constituency grammars? While past work has shown substantial gains from multimodal cues, we investigate whether such gains persist in the presence of rich information from large language models (LLMs). We find that our approach, LLM-based C-PCFG (LC-PCFG), outperforms previous multi-modal methods on the task of unsupervised constituency parsing, achieving state-of-the-art performance on a variety of datasets. Moreover, LC-PCFG results in an over 50% reduction in parameter count, and speedups in training time of 1.7x for image-aided models and more than 5x for video-aided models, respectively. These results challenge the notion that extralinguistic signals such as image pixels are needed for unsupervised grammar induction, and point to the need for better text-only baselines in evaluating the need of multi-modality for the task.

We present Masked Audio-Video Learners (MAViL) to train audio-visual representations. Our approach learns with three complementary forms of self-supervision: (1) reconstruction of masked audio and video input data, (2) intra- and inter-modal contrastive learning with masking, and (3) self-training by reconstructing joint audio-video contextualized features learned from the first two objectives. Pre-training with MAViL not only enables the model to perform well in audio-visual classification and retrieval tasks but also improves representations of each modality in isolation, without using information from the other modality for fine-tuning or inference. Empirically, MAViL sets a new state-of-the-art on AudioSet (53.1 mAP) and VGGSound (67.1% accuracy). For the first time, a self-supervised audio-visual model outperforms ones that use external supervision on these benchmarks. Code will be available soon.

Semantic navigation is necessary to deploy mobile robots in uncontrolled environments like our homes, schools, and hospitals. Many learning-based approaches have been proposed in response to the lack of semantic understanding of the classical pipeline for spatial navigation, which builds a geometric map using depth sensors and plans to reach point goals. Broadly, end-to-end learning approaches reactively map sensor inputs to actions with deep neural networks, while modular learning approaches enrich the classical pipeline with learning-based semantic sensing and exploration. But learned visual navigation policies have predominantly been evaluated in simulation. How well do different classes of methods work on a robot? We present a large-scale empirical study of semantic visual navigation methods comparing representative methods from classical, modular, and end-to-end learning approaches across six homes with no prior experience, maps, or instrumentation. We find that modular learning works well in the real world, attaining a 90% success rate. In contrast, end-to-end learning does not, dropping from 77% simulation to 23% real-world success rate due to a large image domain gap between simulation and reality. For practitioners, we show that modular learning is a reliable approach to navigate to objects: modularity and abstraction in policy design enable Sim-to-Real transfer. For researchers, we identify two key issues that prevent today's simulators from being reliable evaluation benchmarks - (A) a large Sim-to-Real gap in images and (B) a disconnect between simulation and real-world error modes - and propose concrete steps forward.

We consider the problem of embodied visual navigation given an image-goal (ImageNav) where an agent is initialized in an unfamiliar environment and tasked with navigating to a location 'described' by an image. Unlike related navigation tasks, ImageNav does not have a standardized task definition which makes comparison across methods difficult. Further, existing formulations have two problematic properties; (1) image-goals are sampled from random locations which can lead to ambiguity (e.g., looking at walls), and (2) image-goals match the camera specification and embodiment of the agent; this rigidity is limiting when considering user-driven downstream applications. We present the Instance-specific ImageNav task (InstanceImageNav) to address these limitations. Specifically, the goal image is 'focused' on some particular object instance in the scene and is taken with camera parameters independent of the agent. We instantiate InstanceImageNav in the Habitat Simulator using scenes from the Habitat-Matterport3D dataset (HM3D) and release a standardized benchmark to measure community progress.

In this work, we show how to learn a visual walking policy that only uses a monocular RGB camera and proprioception. Since simulating RGB is hard, we necessarily have to learn vision in the real world. We start with a blind walking policy trained in simulation. This policy can traverse some terrains in the real world but often struggles since it lacks knowledge of the upcoming geometry. This can be resolved with the use of vision. We train a visual module in the real world to predict the upcoming terrain with our proposed algorithm Cross-Modal Supervision (CMS). CMS uses time-shifted proprioception to supervise vision and allows the policy to continually improve with more real-world experience. We evaluate our vision-based walking policy over a diverse set of terrains including stairs (up to 19cm high), slippery slopes (inclination of 35 degrees), curbs and tall steps (up to 20cm), and complex discrete terrains. We achieve this performance with less than 30 minutes of real-world data. Finally, we show that our policy can adapt to shifts in the visual field with a limited amount of real-world experience. Video results and code at https://antonilo.github.io/vision_locomotion/.

我们探索一种以数据为基础的学习方法来优化神经网络。我们构建神经网络检查点的数据集，并培训有关参数的生成模型。特别是，我们的模型是一个条件扩散变压器，鉴于初始输入参数向量以及提示的丢失，误差或返回，可以预测实现所需度量的参数更新的分布。在测试时，它可以在一个更新中优化具有看不见的参数的神经网络。我们发现我们的方法成功地生成了各种损失提示的参数。此外，它可以采样多模式参数解决方案，并具有有利的缩放属性。我们将方法应用于监督和强化学习中的不同神经网络体系结构和任务。

本文提出了针对四方的通用自适应控制器，可以将其部署为零射击到具有截然不同的质量，手臂长度和运动常数的四轮驱动器，并且还显示出对运行时未知干扰的快速适应。核心算法的想法是学习一个单一的策略，该策略不仅可以在测试时间在线适应无人机的干扰，还可以在同一框架中适用于机器人动力学和硬件。我们通过训练神经网络来估计机器人和环境参数的潜在表示，该参数用于调节控制器的行为，也表示为神经网络。我们专门训练两个网络进行模拟，目的是将四轮驱动器飞往目标位置并避免撞击地面。我们直接在模拟中训练了相同的控制器，而没有对两个四肢旋转器进行任何修改，其中质量，惯性差异差异，最大电动机速度最大为4次。此外，我们显示了四肢和惯性的突然和大型干扰（最高35.7％）的快速适应。我们在模拟和物理世界中进行了广泛的评估，在该评估中，我们的表现优于最先进的基于学习的自适应控制器和专门针对每个平台的传统PID控制器。视频结果可以在https://dz298.github.io/universal-drone-controller/上找到。