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Found 9 papers, 3 papers with code
Decentralized Gossip Mutual Learning (GML) for brain tumor segmentation on multi-parametric MRI
Federated Learning (FL) enables collaborative model training among medical centers without sharing private data.
Decentralized Gossip Mutual Learning (GML) for automatic head and neck tumor segmentation
Federated learning (FL) has emerged as a promising strategy for collaboratively training complicated machine learning models from different medical centers without the need of data sharing.
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data.
Automatic Brain Tumor Segmentation with Scale Attention Network
Automatic segmentation of brain tumors is an essential but challenging step for extracting quantitative imaging biomarkers for accurate tumor detection, diagnosis, prognosis, treatment planning and assessment.
A Deep Regression Model for Seed Identification in Prostate Brachytherapy
Post-implant dosimetry (PID) is an essential step of prostate brachytherapy that utilizes CT to image the prostate and allow the location and dose distribution of the radioactive seeds to be directly related to the actual prostate.
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.
Hierarchical Convolutional-Deconvolutional Neural Networks for Automatic Liver and Tumor Segmentation
MICCAI 2017 Liver Tumor Segmentation Challenge (LiTS) provides a common platform for comparing different automatic algorithms on contrast-enhanced abdominal CT images in tasks including 1) liver segmentation, 2) liver tumor segmentation, and 3) tumor burden estimation.
Automatic Liver And Tumor Segmentation
Liver Segmentation
+2
Improving Dermoscopic Image Segmentation with Enhanced Convolutional-Deconvolutional Networks
Automatic skin lesion segmentation on dermoscopic images is an essential step in computer-aided diagnosis of melanoma.
Automatic skin lesion segmentation with fully convolutional-deconvolutional networks
This paper summarizes our method and validation results for the ISBI Challenge 2017 - Skin Lesion Analysis Towards Melanoma Detection - Part I: Lesion Segmentation
Ranked #1 on
Lesion Segmentation
on ISIC 2017
