Current whole slide image (WSI) segmentation aims at extracting tumor regions from the background. Unlike this, segmenting distinct tumor areas (instances) within a WSI driven by limited annotated data remains under-explored. In this paper, we formally propose semi-supervised instance segmentation (Semi-IS) in WSIs. We address a key challenge: learning intra-class similarity and inter-class dissimilarity driven by unlabeled data. Specifically, we generally perceive the patch as composed of tokens (together), not the patch alone. We employ contrastive learning to develop a segmentation framework. In the Semi-IS, we find that the boundaries of segmented instances are usually disturbed by noise. We jointly eliminate and preserve noise features to address this problem. We conduct extensive experiments to evaluate the effectiveness and generalizability of Semi-IS, including histopathology and cellular pathology. The results show that in clinical multi instance segmentation tasks, Semi-IS achieves almost full-supervised state-of-the-art results with only 30% annotated data. Semi-IS can improve segmentation accuracy by about 2% on public cell pathology datasets.

A major challenge for learning Bayesian networks is the complexity in searching the huge space of models and parameters. The computational cost is higher when the model topology is more flexible. In this paper, we propose the notion of dominance which can lead to strong pruning of the search space and significant reduction of learning complexity, and apply this notion to the generalized latent class (GLC) models, a class of Bayesian networks for clustering categorical data. GLC models can address the local dependence problem in latent class analysis by assuming a very general graph structure. However, The flexible topology of GLC leads to large increase of the learning complexity. We first propose the concept of dominance and related theoretical results which is general for all Bayesian networks. Based on dominance, we propose an efficient learning algorithm for GLC. A core technique to prune dominated models is regularization, which can eliminate dominated models, leading to significant pruning of the search space. Significant improvements on the modeling quality and time complexity on real datasets are reported.

MOTIVATION: Microarrays have been widely used to discover novel disease related genes. Some types of microarray, such as cDNA arrays, usually contain a considerable portion of missing values. When missing value imputation and gene prioritization are sequentially conducted, it is necessary to consider the distribution space of prioritization scores due to the existence of missing values. We propose an ensemble approach to address this issue. A bootstrap procedure enables us to generate a resample multivariate distribution of the prioritization scores and then to obtain the expected prioritization scores. RESULTS: We used a published microarray two-sample data set to illustrate our approach. We focused on the following issues after missing value imputation: (i) concordance of gene prioritization and (ii) control of true and false positives. We compared our approach with the traditional non-ensemble approach to missing value imputation. We also evaluated the performance of non-imputation approach when the theoretical test distribution was available. The results showed that the ensemble imputation approach provided clearly improved performances in the concordance of gene prioritization and the control of true/false positives, especially when sample sizes were about 5-10 per group and missing rates were about 10-20%, which was a common situation for cDNA microarray studies. AVAILABILITY: The Matlab codes are freely available at http://home.gwu.edu/~ylai/research/Missing.