Post #619

MICCAI 2021 graph papers Here is a guest post by Mahsa Ghorbani about applications of graphs to medical images. A few weeks ago, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) published the accepted list of papers and their reviews. About 20 of the papers are graph related which shows the impact of graph-based methods in medical applications. Here are two examples of papers: - GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent Inference Recently, graphs neural networks show great success in analyzing multi-modal medical data (such as demographic and imaging data) associated with a group of patients regarding the disease prediction problem. However, the conventional methods exhibit poor performance when the graph modality is not available during the inference time. GKD proposes a novel semi-supervised method inspired by the knowledge distillation framework to face this issue. The teacher block distills all the available information in training data and then transfers it to the student network trained with input features, not the filtered ones. Therefore, the student works well on the test data without the graph between them. GKD also utilizes a modified label-propagation algorithm in the teacher block to keep a balance between neighborhood features and node features. - Early Detection of Liver Fibrosis Using Graph Convolutional Networks Fibrosis refers to the deposition of collagen in tissue which can lead to organ dysfunction and even to organ failure. Typically, histochemical stains are being used to visualize collagen. Detection of early onset of fibrosis is critical to detecting long-term damage to identify potential loss of organ function. This paper uses a collagen segmentation method to extract a collagen map from an input histopathological image and then decompose it into a set of tiles. Then cluster the tiles and classify the clusters based on a few samples in them (visually). The tiles clustered as dense collagen are used as the centers, and each tile will be connected to the closest center (Voronoi tessellation). After a set of graph convolutional layers, an attention mechanism is used to aggregate the tile features and detect the fibrosis stage of the input image.