Related references
Note: Only part of the references are listed.Deep neural network for automatic characterization of lesions on 68Ga-PSMA-11 PET/CT
Yu Zhao et al.
EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2020)
Co-Learning Feature Fusion Maps From PET-CT Images of Lung Cancer
Ashnil Kumar et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2020)
RefineNet: Multi-Path Refinement Networks for Dense Prediction
Guosheng Lin et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2020)
Salient Object Detection with Recurrent Fully Convolutional Networks
Linzhao Wang et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2019)
Multiple Resolution Residually Connected Feature Streams for Automatic Lung Tumor Segmentation From CT Images
Jue Jiang et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2019)
Wider or Deeper: Revisiting the ResNet Model for Visual Recognition
Zifeng Wu et al.
PATTERN RECOGNITION (2019)
Deep Learning-Based Image Segmentation on Multimodal Medical Imaging
Zhe Guo et al.
IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES (2019)
Tumor co-segmentation in PET/CT using multi-modality fully convolutional neural network
Xiangming Zhao et al.
PHYSICS IN MEDICINE AND BIOLOGY (2019)
Automated Whole-Body Bone Lesion Detection for Multiple Myeloma on 68Ga-Pentixafor PET/CT Imaging Using Deep Learning Methods
Lina Xu et al.
CONTRAST MEDIA & MOLECULAR IMAGING (2018)
Fully Convolutional Networks for Semantic Segmentation
Evan Shelhamer et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2017)
DCAN: Deep contour-aware networks for object instance segmentation from histology images
Hao Chen et al.
MEDICAL IMAGE ANALYSIS (2017)
A survey on deep learning in medical image analysis
Geert Litjens et al.
MEDICAL IMAGE ANALYSIS (2017)
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
Hoo-Chang Shin et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2016)
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky et al.
INTERNATIONAL JOURNAL OF COMPUTER VISION (2015)
A New Method of Detecting Pulmonary Nodules with PET/CT Based on an Improved Watershed Algorithm
Juanjuan Zhao et al.
PLOS ONE (2015)
Joint segmentation of anatomical and functional images: Applications in quantification of lesions from PET, PET-CT, MRI-PET, and MRI-PET-CT images
Ulas Bagci et al.
MEDICAL IMAGE ANALYSIS (2013)
A Multistage Discriminative Model for Tumor and Lymph Node Detection in Thoracic Images
Yang Song et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2012)
Automatic detection and classification of nasopharyngeal carcinoma on PET/CT with support vector machine
Bangxian Wu et al.
INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY (2012)
Integrated PET/CT in the staging of nonsmall cell lung cancer: technical aspects and clinical integration
W. De Wever et al.
EUROPEAN RESPIRATORY JOURNAL (2009)
From RECIST to PERCIST: Evolving Considerations for PET Response Criteria in Solid Tumors
Richard L. Wahl et al.
JOURNAL OF NUCLEAR MEDICINE (2009)
PET/CT: Form and function
Todd M. Blodgett et al.
RADIOLOGY (2007)
Share Paper
