39 research outputs found
A Joint Graph and Image Convolution Network for Automatic Brain Tumor Segmentation
We present a joint graph convolution-image convolution neural network as our
submission to the Brain Tumor Segmentation (BraTS) 2021 challenge. We model
each brain as a graph composed of distinct image regions, which is initially
segmented by a graph neural network (GNN). Subsequently, the tumorous volume
identified by the GNN is further refined by a simple (voxel) convolutional
neural network (CNN), which produces the final segmentation. This approach
captures both global brain feature interactions via the graphical
representation and local image details through the use of convolutional
filters. We find that the GNN component by itself can effectively identify and
segment the brain tumors. The addition of the CNN further improves the median
performance of the model by 2 percent across all metrics evaluated. On the
validation set, our joint GNN-CNN model achieves mean Dice scores of 0.89,
0.81, 0.73 and mean Hausdorff distances (95th percentile) of 6.8, 12.6, 28.2mm
on the whole tumor, core tumor, and enhancing tumor, respectively.Comment: 9 pages, 3 figures, submitted to BrainLes Workshop (MICCAI 2021) as
part of BraTS2021 challeng
One-Versus-Others Attention: Scalable Multimodal Integration
Multimodal learning models have become increasingly important as they surpass
single-modality approaches on diverse tasks ranging from question-answering to
autonomous driving. Despite the importance of multimodal learning, existing
efforts focus on NLP applications, where the number of modalities is typically
less than four (audio, video, text, images). However, data inputs in other
domains, such as the medical field, may include X-rays, PET scans, MRIs,
genetic screening, clinical notes, and more, creating a need for both efficient
and accurate information fusion. Many state-of-the-art models rely on pairwise
cross-modal attention, which does not scale well for applications with more
than three modalities. For modalities, computing attention will result in
operations, potentially requiring considerable amounts of
computational resources. To address this, we propose a new domain-neutral
attention mechanism, One-Versus-Others (OvO) attention, that scales linearly
with the number of modalities and requires only attention operations, thus
offering a significant reduction in computational complexity compared to
existing cross-modal attention algorithms. Using three diverse real-world
datasets as well as an additional simulation experiment, we show that our
method improves performance compared to popular fusion techniques while
decreasing computation costs
Jointly Embedding Multiple Single-Cell Omics Measurements
Many single-cell sequencing technologies are now available, but it is still difficult to apply multiple sequencing technologies to the same single cell. In this paper, we propose an unsupervised manifold alignment algorithm, MMD-MA, for integrating multiple measurements carried out on disjoint aliquots of a given population of cells. Effectively, MMD-MA performs an in silico co-assay by embedding cells measured in different ways into a learned latent space. In the MMD-MA algorithm, single-cell data points from multiple domains are aligned by optimizing an objective function with three components: (1) a maximum mean discrepancy (MMD) term to encourage the differently measured points to have similar distributions in the latent space, (2) a distortion term to preserve the structure of the data between the input space and the latent space, and (3) a penalty term to avoid collapse to a trivial solution. Notably, MMD-MA does not require any correspondence information across data modalities, either between the cells or between the features. Furthermore, MMD-MA\u27s weak distributional requirements for the domains to be aligned allow the algorithm to integrate heterogeneous types of single cell measures, such as gene expression, DNA accessibility, chromatin organization, methylation, and imaging data. We demonstrate the utility of MMD-MA in simulation experiments and using a real data set involving single-cell gene expression and methylation data