Abstract Computational pathology foundation models (CPathFMs) have emerged as a powerful approach for analyzing histopathological data, leveraging self-supervised learning to extract robust feature representations from unlabeled whole-slide images. These models, categorized into uni-modal and multi-modal frameworks, have demonstrated promise in automating complex pathology tasks such as segmentation, classification, and biomarker discovery. However, the development of CPathFMs presents significant challenges, such as limited data accessibility, high variability across datasets, the necessity for domain-specific adaptation, and the lack of standardized evaluation benchmarks. This survey provides a comprehensive review of CPathFMs in computational pathology, focusing on pre-training datasets, adaptation strategies, and evaluation tasks. We analyze key techniques, such as contrastive learning, masked image modeling and multi-modal integration, and highlight existing gaps in current research. Finally, we explore future directions from four perspectives for advancing CPathFMs. This survey serves as a valuable resource for researchers, clinicians, and AI practitioners, guiding the advancement of CPathFMs toward robust and clinically applicable AI-driven pathology solutions.

We present a novel edge sparsification approach for semi-supervised learning on undirected and attributed graphs. The main challenge is to retain few edges while minimizing the loss of node classification accuracy. The task can be mathematically formulated as a bi-level optimization problem. We propose to use meta-gradients, which have traditionally been used in meta-learning, to solve the optimization problem, specifically, treating the graph adjacency matrix as hyperparameters to optimize. Experimental results show the effectiveness of the proposed approach. Remarkably, with the resulting sparse and light graph, in many cases the classification accuracy is significantly improved.

A common task in the analysis of data is to compute an approximate embedding of the data in a low-dimensional subspace. The standard algorithm for computing this subspace is the well-known Principal Component Analysis (PCA). PCA can be extended to the case where some data points are viewed as “outliers” that can be ignored, allowing the remaining data points (inliers”) to be more tightly embedded. We develop a new algorithm that detects outliers so that they can be removed prior to applying PCA. The main idea is to rank each point by looking ahead and evaluating the change in the global PCA error if that point is considered as an outlier. Our technical contribution is showing that this lookahead procedure can be implemented efficiently, producing an accurate algorithm with running time not much above the running time of standard PCA algorithms.

We consider popular dimension reduction techniques that project data on a low dimensional subspace. They include Principal Component Analysis, Column Subset Selection, and Johnson-Lindenstrauss projections. These techniques have been classically used to efficiently compute various approximations. We propose the following three-step procedure for enhancing the accuracy of such approximations: 1. Unknown quantities in the approximation are replaced with random variables. 2. The Maximum Entropy method is applied to infer the most likely probability distribution. 3. Expected values of the random variables are used to compute the enhanced estimates. Our use of the Maximum Entropy method requires knowledge of vector norms that can be easily computed during the dimension reduction. We demonstrate significant enhancements in average accuracy for Euclidean distance and Mahalanobis distance, and improvements in evaluating k-nearest neighbors and k-furthest neighbors by using the enhanced Euclidean distance formula.