Context-aware Multiple Instance Learning (MIL) is gaining popularity in Whole Slide Image (WSI) classification. Existing methods typically convert instances in a WSI into one-dimensional sequences and learn the long-range contextual dependencies among instances. However, due to the extremely large size of WSIs and the morphological similarities within tissue structures, the enormous number of redundant instances significantly increases computational overhead in the context learning paradigm. Additionally, the rearrangement of instances into one dimension loses the inherent spatial information involved in image patches, further compromising the classification performance of pathological images. Consequently, efficiently modeling contextual dependencies in WSIs remains a crucial challenge. In this paper, we propose a novel Semantic Anchor-based Context-aware Multiple Instance Learning (SeCoMIL) framework. This framework partitions the WSI into a series of regions and encodes the coordinates of instances within these regions to preserve their spatial relationships. Subsequently, SeCoMIL identifies the most representative instances from each region as semantic anchors. By capturing both the local context around these anchors and the global context across different anchors, the framework efficiently summarizes the critical pathological information of the WSI, enabling precise classification. Extensive experiments on four public datasets (CAMELYON16, CAMELYON17, TCGA-NSCLC, and TCGA-RCC) demonstrate the robustness of our method, with superior performance compared to state-of-the-art methods.

A Join-Project operation is a join operation followed by a duplicate eliminating projection operation. It is used in a large variety of applications, including entity matching, set analytics, and graph analytics. Previous work proposes a hybrid design that exploits the classical solution (i.e., join and deduplication), and MM (matrix multiplication) to process the sparse and the dense portions of the input data, respectively. However, we observe three problems in the state-of-the-art solution: 1) The outputs of the sparse and dense portions overlap, requiring an extra deduplication step; 2) Its table-to-matrix transformation makes an over-simplified assumption of the attribute values; and 3) There is a mismatch between the employed MM in BLAS packages and the characteristics of the Join-Project operation. In this paper, we propose DIM 3 , an optimized algorithm for the Join-Project operation. To address 1), we propose an intersection-free partition method to completely remove the final deduplication step. For 2), we develop an optimized design for mapping attribute values to natural numbers. For 3), we propose DenseEC and SparseBMM algorithms to exploit the structure of Join-Project for better efficiency. Moreover, we extend DIM 3 to consider partial result caching and support Join- op queries, including Join-Aggregate and MJP (Multi-way Joins with Projection). Experimental results using both real-world and synthetic data sets show that DIM 3 outperforms previous Join-Project solutions by a factor of 2.3X-18X. Compared to RDBMSs, DIM 3 achieves orders of magnitude speedups.