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The task of medical image segmentation presents unique challenges,necessitating both localized and holistic semantic understanding to accuratelydelineate areas of interest, such as critical tissues or aberrant features.This complexity is heightened in medical image segmentation due to the highdegree of inter-class similarities, intra-class variations, and possible imageobfuscation. The segmentation task further diversifies when considering thestudy of histopathology slides for autoimmune diseases like dermatomyositis.The analysis of cell inflammation and interaction in these cases has been lessstudied due to constraints in data acquisition pipelines. Despite theprogressive strides in medical science, we lack a comprehensive collection ofautoimmune diseases. As autoimmune diseases globally escalate in prevalence andexhibit associations with COVID-19, their study becomes increasingly essential.While there is existing research that integrates artificial intelligence in theanalysis of various autoimmune diseases, the exploration of dermatomyositisremains relatively underrepresented. In this paper, we present a deep-learningapproach tailored for Medical image segmentation. Our proposed methodoutperforms the current state-of-the-art techniques by an average of 12.26% forU-Net and 12.04% for U-Net++ across the ResNet family of encoders on thedermatomyositis dataset. Furthermore, we probe the importance of optimizingloss function weights and benchmark our methodology on three challengingmedical image segmentation tasks

Enhancing Medical Image Segmentation: Optimizing Cross-Entropy Weights and Post-Processing with Autoencoders