With the wide application of sparse ToF sensors in mobile devices, RGB
image-guided sparse depth completion has attracted extensive attention
recently, but still faces some problems. First, the fusion of multimodal
information requires more network modules to process different modalities. But
the application scenarios of sparse ToF measurements usually demand lightweight
structure and low computational cost. Second, fusing sparse and noisy depth
data with dense pixel-wise RGB data may introduce artifacts. In this paper, a
light but efficient depth completion network is proposed, which consists of a
two-branch global and local depth prediction module and a funnel convolutional
spatial propagation network. The two-branch structure extracts and fuses
cross-modal features with lightweight backbones. The improved spatial
propagation module can refine the completed depth map gradually. Furthermore,
corrected gradient loss is presented for the depth completion problem.
Experimental results demonstrate the proposed method can outperform some
state-of-the-art methods with a lightweight architecture. The proposed method
also wins the championship in the MIPI2022 RGB+TOF depth completion challenge.

本文提出了一种轻量的深度完成网络，包括两个分支全局和局部深度预测模块和漏斗卷积空间传播网络，通过轻量级骨干提取和融合交叉模式特征，改进了空间传播模块可以逐步改善修饰后的深度图，解决了 RGB 图像引导的稀疏深度完成问题，通过修正的梯度损失解决了深度完成问题，并在 MIPI2022 RGB + TOF 深度完成挑战中获得了冠军。

学习高效的多模态深度完成模型

Learning an Efficient Multimodal Depth Completion Model

Cancer genomes exhibit a large number of different alterations that affect
many genes in a diverse manner. It is widely believed that these alterations
follow combinatorial patterns that have a strong connection with the underlying
molecular interaction networks and functional pathways. A better understanding
of the generative mechanisms behind the mutation rules and their influence on
gene communities is of great importance for the process of driver mutations
discovery and for identification of network modules related to cancer
development and progression. We developed a new method for cancer mutation
pattern analysis based on a constrained form of correlation clustering.
Correlation clustering is an agnostic learning method that can be used for
general community detection problems in which the number of communities or
their structure is not known beforehand. The resulting algorithm, named $C^3$,
leverages mutual exclusivity of mutations, patient coverage, and driver network
concentration principles; it accepts as its input a user determined combination
of heterogeneous patient data, such as that available from TCGA (including
mutation, copy number, and gene expression information), and creates a large
number of clusters containing mutually exclusive mutated genes in a particular
type of cancer. The cluster sizes may be required to obey some useful soft size
constraints, without impacting the computational complexity of the algorithm.
To test $C^3$, we performed a detailed analysis on TCGA breast cancer and
glioblastoma data and showed that our algorithm outperforms the
state-of-the-art CoMEt method in terms of discovering mutually exclusive gene
modules and identifying driver genes. Our $C^3$ method represents a unique tool
for efficient and reliable identification of mutation patterns and driver
pathways in large-scale cancer genomics studies.

本研究开发了基于相互排斥、患者覆盖和驱动因子网络浓度原则的 $C^3$ 算法，可以在大规模癌症基因组研究中更高效、可靠地识别突变模式和驱动通路，并在 TCGA 乳腺癌和胶质母细胞瘤数据上的测试中优于 CoMEt 方法。