Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge.
Spyridon BakasMauricio ReyesAndrás JakabStefan BauerMarkus RempflerAlessandro CrimiRussell Takeshi ShinoharaChristoph BergerSung Min HaMartin RozyckiMarcel PrastawaEsther AlbertsJana LipkováJohn B. FreymannJustin S. KirbyMichel BilelloHassan M. Fathallah-ShaykhRoland WiestJan KirschkeBenedikt WiestlerRivka R. ColenAikaterini KotrotsouPamela LaMontagneDaniel S. MarcusMikhail MilchenkoArash NazeriMarc-André WeberAbhishek MahajanUjjwal BaidDongjin KwonManu AgarwalMahbubul AlamAlberto AlbiolAntonio AlbiolAlex VargheseTran Anh TuanTal ArbelAaron AveryPranjal B.Subhashis BanerjeeThomas BatchelderKayhan N. BatmanghelichEnzo BattistellaMartin BendszusEze BensonJosé BernalGeorge BirosMariano CabezasSiddhartha ChandraYi-Ju Changet al.Published in: CoRR (2018)
Keyphrases
- machine learning algorithms
- survival prediction
- brain tumor segmentation
- benchmark data sets
- learning algorithm
- machine learning
- decision trees
- machine learning methods
- learning problems
- random forests
- lung cancer
- predictive accuracy
- gene selection
- machine learning approaches
- learning tasks
- machine learning systems
- gene expression
- input features
- magnetic resonance images
- medical images
- statistical machine learning
- learning models
- machine learning models
- training data
- image processing
- data mining