TY - GEN
T1 - A Multi-task Deep Learning Framework to Localize the Eloquent Cortex in Brain Tumor Patients Using Dynamic Functional Connectivity
AU - Nandakumar, Naresh
AU - D’Souza, Niharika Shimona
AU - Manzoor, Komal
AU - Pillai, Jay J.
AU - Gujar, Sachin K.
AU - Sair, Haris I.
AU - Venkataraman, Archana
N1 - Funding Information:
Acknowledgement. This work was supported by the National Science Foundation CAREER award 1845430 (PI: Venkataraman) and the Research & Education Foundation Carestream Health RSNA Research Scholar Grant RSCH1420.
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - We present a novel deep learning framework that uses dynamic functional connectivity to simultaneously localize the language and motor areas of the eloquent cortex in brain tumor patients. Our method leverages convolutional layers to extract graph-based features from the dynamic connectivity matrices and a long-short term memory (LSTM) attention network to weight the relevant time points during classification. The final stage of our model employs multi-task learning to identify different eloquent subsystems. Our unique training strategy finds a shared representation between the cognitive networks of interest, which enables us to handle missing patient data. We evaluate our method on resting-state fMRI data from 56 brain tumor patients while using task fMRI activations as surrogate ground-truth labels for training and testing. Our model achieves higher localization accuracies than conventional deep learning approaches and can identify bilateral language areas even when trained on left-hemisphere lateralized cases. Hence, our method may ultimately be useful for preoperative mapping in tumor patients.
AB - We present a novel deep learning framework that uses dynamic functional connectivity to simultaneously localize the language and motor areas of the eloquent cortex in brain tumor patients. Our method leverages convolutional layers to extract graph-based features from the dynamic connectivity matrices and a long-short term memory (LSTM) attention network to weight the relevant time points during classification. The final stage of our model employs multi-task learning to identify different eloquent subsystems. Our unique training strategy finds a shared representation between the cognitive networks of interest, which enables us to handle missing patient data. We evaluate our method on resting-state fMRI data from 56 brain tumor patients while using task fMRI activations as surrogate ground-truth labels for training and testing. Our model achieves higher localization accuracies than conventional deep learning approaches and can identify bilateral language areas even when trained on left-hemisphere lateralized cases. Hence, our method may ultimately be useful for preoperative mapping in tumor patients.
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U2 - 10.1007/978-3-030-66843-3_4
DO - 10.1007/978-3-030-66843-3_4
M3 - Conference contribution
AN - SCOPUS:85101584451
SN - 9783030668426
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 34
EP - 44
BT - Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology - 3rd International Workshop, MLCN 2020, and Second International Workshop, RNO-AI 2020, Held in Conjunction with MICCAI 2020, Proceedings
A2 - Kia, Seyed Mostafa
A2 - Mohy-ud-Din, Hassan
A2 - Abdulkadir, Ahmed
A2 - Bass, Cher
A2 - Habes, Mohamad
A2 - Rondina, Jane Maryam
A2 - Tax, Chantal
A2 - Wang, Hongzhi
A2 - Wolfers, Thomas
A2 - Rathore, Saima
A2 - Ingalhalikar, Madhura
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2020, and 2nd International Workshop on Radiogenomics in Neuro-oncology, RNO-AI 2020, held in conjunction with MICCAI 2020
Y2 - 4 October 2020 through 8 October 2020
ER -