Enhancing Medical Image Segmentation: Optimizing Cross-Entropy Weights
  and Post-Processing with Autoencoders
Pranav Singh Luoyao Chen Mei Chen Jinqian Pan Raviteja Chukkapalli Shravan Chaudhari Jacopo Cirrone
The task of medical image segmentation presents unique challenges,
necessitating both localized and holistic semantic understanding to accurately
delineate areas of interest, such as critical tissues or aberrant features.
This complexity is heightened in medical image segmentation due to the high
degree of inter-class similarities, intra-class variations, and possible image
obfuscation. The segmentation task further diversifies when considering the
study of histopathology slides for autoimmune diseases like dermatomyositis.
The analysis of cell inflammation and interaction in these cases has been less
studied due to constraints in data acquisition pipelines. Despite the
progressive strides in medical science, we lack a comprehensive collection of
autoimmune diseases. As autoimmune diseases globally escalate in prevalence and
exhibit associations with COVID-19, their study becomes increasingly essential.
While there is existing research that integrates artificial intelligence in the
analysis of various autoimmune diseases, the exploration of dermatomyositis
remains relatively underrepresented. In this paper, we present a deep-learning
approach tailored for Medical image segmentation. Our proposed method
outperforms the current state-of-the-art techniques by an average of 12.26% for
U-Net and 12.04% for U-Net++ across the ResNet family of encoders on the
dermatomyositis dataset. Furthermore, we probe the importance of optimizing
loss function weights and benchmark our methodology on three challenging
medical image segmentation tasks