Deep Learning-Based Cancer Detection-Recent Developments,Trend and Challenges

Cancer is one of the most critical diseases that has caused several deaths in today’s world.In most cases,doctors and practitioners are only able to diagnose cancer in its later stages.In the later stages,planning cancer treatment and increasing the patient’s survival rate becomes a very challenging task.Therefore,it becomes the need of the hour to detect cancer in the early stages for appropriate treatment and surgery planning.Analysis and interpretation of medical images such as MRI and CT scans help doctors and practitioners diagnose many diseases,including cancer disease.However,manual interpretation of medical images is costly,time-consuming and biased.Nowadays,deep learning,a subset of artificial intelligence,is gaining increasing attention from practitioners in automatically analysing and interpreting medical images without their intervention.Deep learning methods have reported extraordinary results in different fields due to their ability to automatically extract intrinsic features from images without any dependence on manually extracted features.This study provides a comprehensive review of deep learning methods in cancer detection and diagnosis,mainly focusing on breast cancer,brain cancer,skin cancer,and prostate cancer.This study describes various deep learningmodels and steps for applying deep learningmodels in detecting cancer.Recent developments in cancer detection based on deep learning methods have been critically analysed and summarised to identify critical challenges in applying them for detecting cancer accurately in the early stages.Based on the identified challenges,we provide a few promising future research directions for fellow researchers in the field.The outcome of this study provides many clues for developing practical and accurate cancer detection systems for its early diagnosis and treatment planning.

Curricular Robust Reinforcement Learning via GAN-Based Perturbation Through Continuously Scheduled Task Sequence

Reinforcement learning(RL),one of three branches of machine learning,aims for autonomous learning and is now greatly driving the artificial intelligence development,especially in autonomous distributed systems,such as cooperative Boston Dynamics robots.However,robust RL has been a challenging problem of reliable aspects due to the gap between laboratory simulation and real world.Existing efforts have been made to approach this problem,such as performing random environmental perturbations in the learning process.However,one cannot guarantee to train with a positive perturbation as bad ones might bring failures to RL.In this work,we treat robust RL as a multi-task RL problem,and propose a curricular robust RL approach.We first present a generative adversarial network(GAN)based task generation model to iteratively output new tasks at the appropriate level of difficulty for the current policy.Furthermore,with these progressive tasks,we can realize curricular learning and finally obtain a robust policy.Extensive experiments in multiple environments demonstrate that our method improves the training stability and is robust to differences in training/test conditions.