Multiple Myeloma Cancer Cell Instance Segmentation

TL;DR

This paper develops a deep learning-based system for detecting and segmenting Multiple Myeloma cancer cells in microscopic images, aiming to improve accuracy and efficiency over manual analysis.

Contribution

It introduces a novel architecture that leverages existing object detection and segmentation models for improved performance on cancer cell identification.

Findings

Achieved competitive detection and segmentation accuracy.

Utilized a curated dataset of microscopic cell images.

Demonstrated the effectiveness of the proposed architecture.

Abstract

Images remain the largest data source in the field of healthcare. But at the same time, they are the most difficult to analyze. More than often, these images are analyzed by human experts such as pathologists and physicians. But due to considerable variation in pathology and the potential fatigue of human experts, an automated solution is much needed. The recent advancement in Deep learning could help us achieve an efficient and economical solution for the same. In this research project, we focus on developing a Deep Learning-based solution for detecting Multiple Myeloma cancer cells using an Object Detection and Instance Segmentation System. We explore multiple existing solutions and architectures for the task of Object Detection and Instance Segmentation and try to leverage them and come up with a novel architecture to achieve comparable and competitive performance on the required task. To train our model to detect and segment Multiple Myeloma cancer cells, we utilize a dataset curated by us using microscopic images of cell slides provided by Dr.Ritu Gupta(Prof., Dept. of Oncology AIIMS).