http://journals.andromedapublisher.com/index.php/JMLFS <p><em><span style="font-size: 11.0pt;">JMLFS is a peer-reviewed, open access journal which specialises on ML driven advances in any of the <strong><span style="font-weight: normal;">fundamental sciences</span></strong>, here intended as mathematics, <strong><span style="font-weight: normal;">physics</span></strong>, chemistry and biology. The latter are organised in four&nbsp; subsections, called A, B, C &amp; D, each with at least one editor, with no distinction between theoretical and experimental content. JMLFS publishes submitted articles of letter type, occasional special/topical issues (by invitation) and will consider publishing proceedings (upon enquiry).</span></em></p> Andromeda Publishing And Academic Services LTD en-US Journal of Machine Learning in Fundamental Sciences 2632-2714 <p>Journal of Machine Learning in Fundamental Sciences (JMLFS) is an open access journal published by Andromeda Publishing and Education Services. The articles in JMLFS are distributed according to the terms of&nbsp;<a href="http://creativecommons.org/licenses/by/4.0" target="_blank" rel="noopener">the creative commons license CC-BY 4.0</a>. Under the terms of this license, copyright is retained by the author while use, distribution and reproduction in any medium are permitted provided proper credit is given to original authors and sources.</p> <h2 style="margin-bottom: 0px; margin-top: 0px;">Terms of Submission</h2> <p style="margin-top: 0px; margin-bottom: 0px;" align="justify">By submitting an article for publication in JMLFS, the submitting author asserts that:</p> <p style="margin-top: 0px; margin-bottom: 0px;" align="justify">1. The article presents original contributions by the author(s) which have not been published previously in a peer-reviewed medium and are not subject to copyright protection.</p> <p style="margin-top: 0px; margin-bottom: 0px;" align="justify">2. The co-authors of the article, if any, as well as any institution whose approval is required, agree to the publication of the article in JMLFS.</p> http://journals.andromedapublisher.com/index.php/JMLFS/article/view/229 <p>Automatic image segmentation is a critical component of medical image analysis, and hence quantifying<br>segmentation performance is crucial. Challenges in medical image segmentation are mainly due to spatial<br>variations of regions to be segmented and an imbalance in distribution of the classes. Commonly used<br>metrics treat all detected pixels indiscriminately. However, pixels in smaller segments must be treated differently from pixels in larger segments, as smaller ones aid in early treatment of associated disease and are<br>also easier to miss. To address this, we propose a novel evaluation metric for segmentation performance,<br>emphasizing smaller segments, by assigning higher weightage to smaller segment pixels. “Small SEGment<br>Emphasized Performance” (SSEGEP) metric, based on weighted false positives, was proposed and evaluated using synthetic images and 4 publicly available clinical datasets of eye (fundus imaging, n = 33),<br>breast (mammogram, n = 108), liver (CT, n = 131), and pancreas cancer (CT, n = 107), where n refers<br>to the number of images from the dataset that were used in the study. Mean opinion score (MOS) was<br>calculated from the scores (5-scale scores) of 33-fundus-image segmentation as assigned by 15 researchers<br>(2–5 years of experience in image analysis). Statistical analysis was performed for the other datasets to<br>quantify the relevance of the proposed approach. Across 33 fundus images, where the largest exudate is<br>1.41% and the smallest is 0.0002% of the image, the proposed metric is 30% closer to MOS, as compared<br>to Dice Similarity Coefficient (DSC). Statistical significance testing resulted in a p value of order 10−18 that<br>shows the significance of SSEGEP for hepatic tumors compared to DSC. The proposed metric is found to<br>perform better for the images having multiple segments of a single class.</p> Ammu Raju Neelam Sinha ##submission.copyrightStatement## http://creativecommons.org/licenses/by/4.0 2022-02-02 2022-02-02 2022 1 10.31526/jmlfs.2022.229