Deep Learning in Computer-Aided Diagnosis and Treatment of Tumors: A Survey

TL;DR

This survey reviews recent deep learning applications across four stages of tumor diagnosis and treatment, highlighting advances, challenges, and future directions in computer-aided oncology.

Contribution

It systematically summarizes deep learning methods tailored to different tumor diagnosis and treatment tasks, emphasizing task-specific improvements and recent progress.

Findings

Deep learning models show high accuracy in tumor detection and classification.

Significant progress in imaging and pathological diagnosis stages.

Challenges include data scarcity and model interpretability.

Abstract

Computer-Aided Diagnosis and Treatment of Tumors is a hot topic of deep learning in recent years, which constitutes a series of medical tasks, such as detection of tumor markers, the outline of tumor leisures, subtypes and stages of tumors, prediction of therapeutic effect, and drug development. Meanwhile, there are some deep learning models with precise positioning and excellent performance produced in mainstream task scenarios. Thus follow to introduce deep learning methods from task-orient, mainly focus on the improvements for medical tasks. Then to summarize the recent progress in four stages of tumor diagnosis and treatment, which named In-Vitro Diagnosis (IVD), Imaging Diagnosis (ID), Pathological Diagnosis (PD), and Treatment Planning (TP). According to the specific data types and medical tasks of each stage, we present the applications of deep learning in the Computer-Aided Diagnosis and Treatment of Tumors and analyzing the excellent works therein. This survey concludes by discussing research issues and suggesting challenges for future improvement.