Review of computer-assisted diagnosis model to classify follicular lymphoma histology.

The field of image processing is experiencing significant advancements to support professionals in analyzing histological images obtained from biopsies. The primary objective is to enhance the process of diagnosis and prognostic evaluations. Various forms of cancer can be diagnosed by employing different segmentation techniques followed by postprocessing approaches that can identify distinct neoplastic areas. Using computer approaches facilitates a more objective and efficient study of experts. The progressive advancement of histological image analysis holds significant importance in modern medicine. This paper provides an overview of the current advances in segmentation and classification approaches for images of follicular lymphoma. This research analyzes the primary image processing techniques utilized in the various stages of preprocessing, segmentation of the region of interest, classification, and postprocessing as described in the existing literature. The study also examines the strengths and weaknesses associated with these approaches. Additionally, this study encompasses an examination of validation procedures and an exploration of prospective future research roads in the segmentation of neoplasias.

Computer-assisted interpretation, in-depth exploration and single cell type annotation of RNA sequence data using k-means clustering algorithm.

At now, the majority of approaches rely on manual techniques for annotating cell types subsequent to clustering the data obtained from single-cell RNA sequencing (scRNA-seq). These approaches require a significant amount of physical exertion and depend substantially on the user's skill, perhaps resulting in uneven outcomes and inconsistency in treatment. In this paper, we provide a computer-assisted interpretation of every single cell of a tissue sample, along with an in-depth exploration of an individual cell's molecular, phenotypic and functional attributes. The paper will also perform k-means clustering followed by silhouette validation based on similar phenotype and functional attributes, and also, cell type annotation is performed, where we match a cell's gene profile against some known database by applying certain statistical conditions. Finally, all the genes are mapped spatially on the tissue sample. This paper is an aid to medicine to know which cells are expressed/not expressed in a tissue sample and their spatial location on the tissue sample.