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Convolutional neural network-based methods have significantly enhanced the segmentation performance of biomedical images in recent years. Nevertheless, medical image segmentation presents a challenge marked by layout specificity, with limited variation between samples in medical datasets but significant variation within each individual sample. This aspect has been often overlooked by many models. Consequently, we propose a novel architecture called Attention based multi-scale nested network (AMNNet), specifically designed for efficient biomedical image segmentation. AMNNet comprises four components: early ReSidual U-CBAM (RSUC) modules and convolutional stages, a MLP stage in latent stage, and Convolutional Block Attention Modules (CBAM) integrated into the decoder stage. We introduce a lightweight CBAM to concentrate on regions proximate to the target and suppress extraneous features without substantial parameter increments. The RSUC module is proposed to combine receptive fields of different sizes, capturing comprehensive contextual information across various scales in medical samples. Extensive experiments conducted on the AMNNet reveal its outperformance compared to prevailing medical image segmentation methods across the ISIC2018, CVC-ClinicDB, CVC-ColonDB, BUSI, and GlaS datasets. Notably, AMNNet achieves Dice Similarity Coefficients (DSC) of 91.35%, 90.01%, 90.80%, 81.61%, and 94.31%, respectively.

Abiclustering problem consists of objects and an attribute vector for each object. Biclustering aims at finding a bicluster--a subset of objects that exhibit similar behavior across a subset of attributes, or vice versa. Biclustering inmatrices with binary entries ("0"/"1") can be simplified into the problem of finding submatrices with entries of "1." In this paper, we consider a variant of the biclustering problem: the k-submatrix partition of binary matrices problem. The input of the problem contains an n × m matrix with entries ("0"/"1") and a constant positive integer k. The k-submatrix partition of binary matrices problem is to find exactly k submatrices with entries of "1" such that these k submatrices are pairwise row and column exclusive and each row (column) in the matrix occurs in exactly one of the k submatrices. We discuss the complexity of the k-submatrix partition of binary matrices problem and show that the problem is NP-hard for any ≥ = 3 by reduction from a biclustering problem in bipartite graphs. [ABSTRACT FROM AUTHOR]