Instruct: Given a scientific paper title, retrieve paper titles that are cited by the given paper.
Query: Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks
U-Net: Convolutional Networks for Biomedical Image Segmentation
A Review of Brain Tumor Segmentation and Detection Techniques through MR Images
automated segmentation of mr images of brain tumors .
Quantitative Analysis of Patch-Based Fully Convolutional Neural Networks for Tissue Segmentation on Brain Magnetic Resonance Imaging
MDU-Net: Multi-scale Densely Connected U-Net for biomedical image segmentation
Automated Detection and Extraction of Brain Tumor from MRI Images
DeepMedic for Brain Tumor Segmentation
Automatic Brain Tumor Grading from MRI Data Using Convolutional Neural Networks and Quality Assessment
Train a 3D U-Net to segment cranial vasculature in CTA volume without manual annotation
Automatic brain tumor segmentation and extraction in MR images
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes
An improved implementation of brain tumor detection using segmentation based on soft computing
Texture segmentation with Fully Convolutional Networks
Unsupervised learning algorithms for MRI brain tumor segmentation
Automatic Semantic Segmentation of Brain Gliomas from MRI Images Using a Deep Cascaded Neural Network
Concatenated and Connected Random Forests With Multiscale Patch Driven Active Contour Model for Automated Brain Tumor Segmentation of MR Images
An Efficient Brain Tumor Detection Algorithm Using Watershed & Thresholding Based Segmentation
3D RoI-aware U-Net for Accurate and Efficient Colorectal Tumor Segmentation
BRAIN TUMOR MRI IMAGE SEGMENTATION AND DETECTION IN IMAGE PROCESSING
Image Segmentation and Classification of MRI Brain Tumor Based on Cellular Automata and Neural Networks
JAVELIN III: Answering Non-Factoid Questions in Japanese
Classifying Online Dating Profiles on Tinder using FaceNet Facial Embeddings
Explicit Reflection in Prolog-Tutor
U-Net: Convolutional Networks for Biomedical Image Segmentation
Data Assimilation Using an Ensemble Kalman Filter Technique