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. This problem can be a challenging endeavor, as a pure segmentation objective tends to produce many irrelevant artifacts or flipped pixels. Our novel multi-loss U-Net reformulation proposes an efficient solution to the aforementioned problem. It introduces an additional loss term at the bottom-most U-Net level, which focuses on providing an auxiliary signal of where to look for distinct CFUs. In general, our experiments show that all probed multi-loss U-Net architectures consistently outperform their single-loss counterparts, which embark on the Dice Similarity Coefficient and Cross-Entropy training losses alone.

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. The latter approach provides a segmentation output map and requires an additional counting procedure to calculate unique segmented regions and detect microbial colonies. However, due to pixel-based targets, it tends to generate irrelevant artifacts or errant pixels, leading to inaccurate and mixed post-processing results. In response to these challenges, this paper proposes a novel hybrid counting approach, incorporating a multi-loss U-Net reformulation and a post-processing Petri dish localization algorithm. Firstly, a unique innovation lies in the multi-loss U-Net reformulation. An additional loss term is introduced in the bottleneck U-Net layer, focusing on the delivery of an auxiliary signal that indicates where to look for distinct CFUs. Secondly, the novel localization algorithm automatically incorporates an agar plate and its bezel into the CFU counting techniques. Finally, the proposition is further enhanced by the integration of a fully automated solution, which comprises a specially designed uniform Petri dish illumination system and a counting web application. The latter application directly receives images from the camera, processes them, and sends the segmentation results to the user. This feature provides an opportunity to correct the CFU counts, offering a feedback loop that contributes to the continued development of the deep learning model. Through extensive experimentation, the authors of this paper have found that all probed multi-loss U-Net architectures incorporated into the proposed hybrid approach consistently outperformed their single-loss counterparts, as well as other comparable models such as self-normalized density maps and YOLOv6, by at least 1% to 3% in mean absolute and symmetric mean absolute percentage errors. Further significant improvements were also reported through the means of the novel localization algorithm. This reaffirms the effectiveness of the proposed hybrid solution in addressing contemporary challenges of precise in vitro CFU counting.

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. In general, our experiments show that the proposed multi-path architecture outperforms other state-of-the-art approaches that embark on similar ideas of pyramid structures, skip-connections, and encoder-decoder pathways. A significant improvement of the Dice similarity coefficient is attained at our proprietary colony-forming unit dataset, where a score of 0.809 was achieved for the foreground class.

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. We address this issue by presenting a new class of SVM-like algorithms which help to approach multi-class classification and novelty detection from a new perspective. We introduce a new coupling term between classes which leverages the problem of finding a good decision boundary while preserving the compactness of a support with the l2-norm penalty. First we present our optimization objective in the primal and then derive a dual QP formulation of the problem. Next we propose a Least-Squares formulation which results in a linear system which drastically reduces computational costs. Finally we derive a Pegasos-based formulation which can effectively cope with large data sets that cannot be handled by many existing QP solvers. We complete our paper with experiments that validate the usefulness and practical importance of the proposed methods both in classification and novelty detection settings.

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. SVM. Moreover, the advantage of such kernels could be potentially exploited for other kernel methods and essential computer-aided tasks such as novelty detection and clustering. Our experimental results and theoretical generalization bounds imply that ranking-based kernels outperform other traditionally employed SVM kernels on high dimensional biomedical and microarray data.