Multi-Loss U-Net Reformulation as an Efficient Solution to the Colony-Forming Unit Counting Problem.

U-Net is undoubtedly the most cited and popularized deep learning architecture in the biomedical domain. Starting with image, volume, or video segmentation in numerous practical applications, such as digital pathology, and continuing to Colony-Forming Unit (CFU) segmentation, new emerging areas require an additional U-Net reformulation to solve some inherent inefficiencies of a simple segmentation-tailored loss function, such as the Dice Similarity Coefficient, being applied at the training step. One of such areas is segmentation-driven CFU counting, where after receiving a segmentation output map one should count all distinct segmented regions belonging to different detected microbial colonies.

Hybrid Approach to Colony-Forming Unit Counting Problem Using Multi-Loss U-Net Reformulation.

Colony-Forming Unit (CFU) counting is a complex problem without a universal solution in biomedical and food safety domains. A multitude of sophisticated heuristics and segmentation-driven approaches have been proposed by researchers. However, U-Net remains the most frequently cited and used deep learning method in these domains.

Multi-Path U-Net Architecture for Cell and Colony-Forming Unit Image Segmentation.

U-Net is the most cited and widely-used deep learning model for biomedical image segmentation. In this paper, we propose a new enhanced version of a ubiquitous U-Net architecture, which improves upon the original one in terms of generalization capabilities, while addressing several immanent shortcomings, such as constrained resolution and non-resilient receptive fields of the main pathway. Our novel multi-path architecture introduces a notion of an individual receptive field pathway, which is merged with other pathways at the bottom-most layer by concatenation and subsequent application of Layer Normalization and Spatial Dropout, which can improve generalization performance for small datasets.

Multi-Class Supervised Novelty Detection.

In this paper we study the problem of finding a support of unknown high-dimensional distributions in the presence of labeling information, called Supervised Novelty Detection (SND). The One-Class Support Vector Machine (SVM) is a widely used kernel-based technique to address this problem. However with the latter approach it is difficult to model a mixture of distributions from which the support might be constituted.

Ranking-based kernels in applied biomedical diagnostics using a support vector machine.

This paper presents some essential findings and results on using ranking-based kernels for the analysis and utilization of high dimensional and noisy biomedical data in applied clinical diagnostics. We claim that presented kernels combined with a state-of-the-art classification technique - a Support Vector Machine (SVM) - could significantly improve the classification rate and predictive power of the wrapper method, e.g.