Beck, A.H.[Andrew H.],
Sangoi, A.R.[Ankur R.],
Leung, S.[Samuel],
Marinelli, R.J.[Robert J.],
Nielsen, T.O.[Torsten O.],
van de Vijver, M.J.[Marc J.],
West, R.B.[Robert B.],
van de Rijn, M.[Matt],
Koller, D.[Daphne],
Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features
Associated with Survival,
Sci. Transl. Med.(3), Issue 108, 9 November 2011, pp. 108ra113
DOI Link
BibRef
1111
Shao, W.[Wei],
Wang, T.X.[Tong-Xin],
Huang, Z.[Zhi],
Han, Z.[Zhi],
Zhang, J.[Jie],
Huang, K.[Kun],
Weakly Supervised Deep Ordinal Cox Model for Survival Prediction From
Whole-Slide Pathological Images,
MedImg(40), No. 12, December 2021, pp. 3739-3747.
IEEE DOI
2112
Predictive models, Cancer, Computational modeling,
Prognostics and health management, Tumors, Hazards,
ordinal cox model
BibRef
Ghani, T.[Tahira],
Oommen, J.B.[John B.],
On utilizing 2D features from 3D scans to enhance the prediction of
lung cancer survival rates,
PRL(152), 2021, pp. 56-62.
Elsevier DOI
2112
Medical image processing, Lung cancer treatment, Prediction of survival rates
BibRef
Ghani, T.[Tahira],
Oommen, B.J.[B. John],
Enhancing the Prediction of Lung Cancer Survival Rates Using 2d
Features from 3d Scans,
ICIAR20(II:202-215).
Springer DOI
2007
BibRef
Kapil, A.[Ansh],
Meier, A.[Armin],
Steele, K.[Keith],
Rebelatto, M.[Marlon],
Nekolla, K.[Katharina],
Haragan, A.[Alexander],
Silva, A.[Abraham],
Zuraw, A.[Aleksandra],
Barker, C.[Craig],
Scott, M.L.[Marietta L.],
Wiestler, T.[Tobias],
Lanzmich, S.[Simon],
Schmidt, G.[Günter],
Brieu, N.[Nicolas],
Domain Adaptation-Based Deep Learning for Automated Tumor Cell (TC)
Scoring and Survival Analysis on PD-L1 Stained Tissue Images,
MedImg(40), No. 9, September 2021, pp. 2513-2523.
IEEE DOI
2109
Tumors, Image segmentation, Training, Deep learning, Task analysis,
Oncology, Immune system, Deep learning, digital pathology,
PD-L1 biomarker
BibRef
Rafi, A.[Asra],
Madni, T.M.[Tahir Mustafa],
Janjua, U.I.[Uzair Iqbal],
Ali, M.J.[Muhammad Junaid],
Abid, M.N.[Muhammad Naeem],
Multi-level dilated convolutional neural network for brain tumour
segmentation and multi-view-based radiomics for overall survival
prediction,
IJIST(31), No. 3, 2021, pp. 1519-1535.
DOI Link
2108
brain disease, brain tumour segmentation, dilated convolution,
magnetic resonance imaging, multi-view, overall survival, random forest
BibRef
Fiaz, K.[Kiran],
Madni, T.M.[Tahir Mustafa],
Anwar, F.[Fozia],
Janjua, U.I.[Uzair Iqbal],
Rafi, A.[Asra],
Abid, M.M.N.[Mian Muhammad Naeem],
Sultana, N.[Nasira],
Brain tumor segmentation and multiview multiscale-based radiomic
model for patient's overall survival prediction,
IJIST(32), No. 3, 2022, pp. 982-999.
DOI Link
2205
brain tumor segmentation, glioblastoma, MRI,
radiomic feature extraction, survival prediction
BibRef
Di, D.L.[Dong-Lin],
Zhang, J.[Jun],
Lei, F.Q.[Fu-Qiang],
Tian, Q.[Qi],
Gao, Y.[Yue],
Big-Hypergraph Factorization Neural Network for Survival Prediction
From Whole Slide Image,
IP(31), 2022, pp. 1149-1160.
IEEE DOI
2202
Feature extraction, Predictive models, Correlation, Hazards,
Data models, Convolutional neural networks, Visualization, survival prediction
BibRef
Ismail, M.[Marwa],
Prasanna, P.[Prateek],
Bera, K.[Kaustav],
Statsevych, V.[Volodymyr],
Hill, V.[Virginia],
Singh, G.[Gagandeep],
Partovi, S.[Sasan],
Beig, N.[Niha],
McGarry, S.[Sean],
Laviolette, P.[Peter],
Ahluwalia, M.[Manmeet],
Madabhushi, A.[Anant],
Tiwari, P.[Pallavi],
Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor
to Characterize Tumor Field Effect: Application to Survival
Prediction in Glioblastoma,
MedImg(41), No. 7, July 2022, pp. 1764-1777.
IEEE DOI
2207
Tumors, Strain, Feature extraction, Training, Radiomics,
Magnetic resonance imaging, Cancer, Glioblastoma, survival,
LASSO
BibRef
Asthana, P.[Pallavi],
Hanmandlu, M.[Madasu],
Vashisth, S.[Sharda],
Brain tumor detection and patient survival prediction using U-Net and
regression model,
IJIST(32), No. 5, 2022, pp. 1801-1814.
DOI Link
2209
biomedical imaging, brain tumor, deep learning, learning model,
regression model, segmentation
BibRef
Ning, Z.Y.[Zhen-Yuan],
Zhao, Z.X.[Zhang-Xin],
Feng, Q.J.[Qian-Jin],
Chen, W.F.[Wu-Fan],
Xiao, Q.[Qing],
Zhang, Y.[Yu],
Mutual-Assistance Learning for Standalone Mono-Modality Survival
Analysis of Human Cancers,
PAMI(45), No. 6, June 2023, pp. 7577-7594.
IEEE DOI
2305
Data models, Representation learning, Cancer,
Prognostics and health management, Analytical models, human cancer
BibRef
Hou, W.T.[Wen-Tai],
Lin, C.X.[Cheng-Xuan],
Yu, L.[Lequan],
Qin, J.[Jing],
Yu, R.S.[Rong-Shan],
Wang, L.S.[Lian-Sheng],
Hybrid Graph Convolutional Network With Online Masked Autoencoder for
Robust Multimodal Cancer Survival Prediction,
MedImg(42), No. 8, August 2023, pp. 2462-2473.
IEEE DOI
2308
Cancer, Biomedical imaging, Predictive models, Data models,
Convolutional neural networks, Genomics, Bioinformatics, decision fusion
BibRef
Kaur, G.[Gurinderjeet],
Rana, P.S.[Prashant Singh],
Arora, V.[Vinay],
Deep learning and machine learning-based early survival predictions
of glioblastoma patients using pre-operative three-dimensional brain
magnetic resonance imaging modalities,
IJIST(33), No. 1, 2023, pp. 340-361.
DOI Link
2301
3D magnetic resonance imaging, brain tumor segmentation,
convolutional neural network, deep learning, machine learning, UNet
BibRef
Zhu, J.Y.[Jing-Yu],
Ye, J.M.[Jian-Ming],
Dong, L.[Leshui],
Ma, X.F.[Xiao-Fei],
Tang, N.[Na],
Xu, P.[Peng],
Jin, W.[Wei],
Li, R.P.[Rui-Peng],
Yang, G.[Guang],
Lai, X.B.[Xiao-Bo],
Non-invasive prediction of overall survival time for glioblastoma
multiforme patients based on multimodal MRI radiomics,
IJIST(33), No. 4, 2023, pp. 1261-1274.
DOI Link
2307
deep learning, glioblastoma multiforme,
magnetic resonance imaging, overall survival time, radiomics
BibRef
Di, D.L.[Dong-Lin],
Zou, C.Q.[Chang-Qing],
Feng, Y.F.[Yi-Fan],
Zhou, H.Y.[Hai-Yan],
Ji, R.R.[Rong-Rong],
Dai, Q.H.[Qiong-Hai],
Gao, Y.[Yue],
Generating Hypergraph-Based High-Order Representations of Whole-Slide
Histopathological Images for Survival Prediction,
PAMI(45), No. 5, May 2023, pp. 5800-5815.
IEEE DOI
2304
Correlation, Feature extraction, Data models, Predictive models,
Convolution, Pathology, Task analysis, High-Order representation,
whole slide image
BibRef
Fan, L.[Lei],
Sowmya, A.[Arcot],
Meijering, E.[Erik],
Song, Y.[Yang],
Cancer Survival Prediction From Whole Slide Images With
Self-Supervised Learning and Slide Consistency,
MedImg(42), No. 5, May 2023, pp. 1401-1412.
IEEE DOI
2305
Feature extraction, Task analysis, Computational modeling, Cancer,
Annotations, Self-supervised learning, Training,
deep learning
BibRef
Shao, W.[Wei],
Zuo, Y.L.[Ying-Li],
Shi, Y.Y.[Yang-Yang],
Wu, Y.W.[Ya-Wen],
Tang, J.[Jiao],
Zhao, J.Y.[Jun-Yong],
Sun, L.[Liang],
Lu, Z.X.[Zi-Xiao],
Sheng, J.P.[Jian-Peng],
Zhu, Q.[Qi],
Zhang, D.Q.[Dao-Qiang],
Characterizing the Survival-Associated Interactions Between
Tumor-Infiltrating Lymphocytes and Tumors From Pathological Images
and Multi-Omics Data,
MedImg(42), No. 10, October 2023, pp. 3025-3035.
IEEE DOI
2310
BibRef
Li, Z.[Zhe],
Jiang, Y.M.[Yu-Ming],
Lu, M.K.[Meng-Kang],
Li, R.J.[Rui-Jiang],
Xia, Y.[Yong],
Survival Prediction via Hierarchical Multimodal Co-Attention
Transformer: A Computational Histology-Radiology Solution,
MedImg(42), No. 9, September 2023, pp. 2678-2689.
IEEE DOI
2310
BibRef
Shao, W.[Wei],
Liu, J.X.[Jian-Xin],
Zuo, Y.L.[Ying-Li],
Qi, S.[Shile],
Hong, H.H.[Hong-Hai],
Sheng, J.P.[Jian-Peng],
Zhu, Q.[Qi],
Zhang, D.Q.[Dao-Qiang],
FAM3L: Feature-Aware Multi-Modal Metric Learning for Integrative
Survival Analysis of Human Cancers,
MedImg(42), No. 9, September 2023, pp. 2552-2565.
IEEE DOI
2310
BibRef
Cui, J.Q.[Jia-Qi],
Zheng, H.[Hanci],
Liu, Y.J.[Yuan-Jun],
Wu, X.[Xi],
Wang, Y.[Yan],
Ma2SP: Missing-Aware Prompting With Modality-Adaptive Integration for
Incomplete Multi-Modal Survival Prediction,
SPLetters(31), 2024, pp. 2455-2459.
IEEE DOI
2410
Feature extraction, Cancer, Tumors, Training, Predictive models,
Task analysis, Data models, Survival prediction,
incomplete multi-modal learning
BibRef
Yun, J.[Juyoung],
Abousamra, S.[Shahira],
Li, C.[Chen],
Gupta, R.[Rajarsi],
Kurc, T.[Tahsin],
Samaras, D.[Dimitris],
van Dyke, A.[Alison],
Saltz, J.[Joel],
Chen, C.[Chao],
Uncertainty Estimation for Tumor Prediction with Unlabeled Data,
CVMI24(6946-6954)
IEEE DOI
2410
Pathology, Uncertainty, Accuracy, Monte Carlo methods,
Neural networks, Estimation, Predictive models,
Digital Pathology
BibRef
Shao, W.[Wei],
Shi, Y.[YangYang],
Zhang, D.Q.[Dao-Qiang],
Zhou, J.[JunJie],
Wan, P.[Peng],
Tumor Micro-Environment Interactions Guided Graph Learning for
Survival Analysis of Human Cancers from Whole-Slide Pathological
Images,
CVPR24(11694-11703)
IEEE DOI
2410
Pathology, Analytical models, Organizations, Predictive models,
Semisupervised learning, Prediction algorithms,
Survival Analysis
BibRef
Xu, Y.X.[Ying-Xue],
Chen, H.[Hao],
Multimodal Optimal Transport-based Co-Attention Transformer with
Global Structure Consistency for Survival Prediction,
ICCV23(21184-21194)
IEEE DOI
2401
BibRef
Zhou, F.T.[Feng-Tao],
Chen, H.[Hao],
Cross-Modal Translation and Alignment for Survival Analysis,
ICCV23(21428-21437)
IEEE DOI
2401
BibRef
Li, X.[Xiang],
Qian, X.L.[Xue-Lin],
Liang, L.T.[Li-Tian],
Kong, L.J.[Ling-Jie],
Dong, Q.[Qiaole],
Chen, J.J.[Jie-Jun],
Liu, D.X.[Ding-Xia],
Yao, X.Z.[Xiu-Zhong],
Fu, Y.W.[Yan-Wei],
Causally-Aware Intraoperative Imputation for Overall Survival Time
Prediction,
CVPR23(15681-15690)
IEEE DOI
2309
BibRef
Dao, D.P.[Duy-Phuong],
Yang, H.J.[Hyung-Jeong],
Ho, N.H.[Ngoc-Huynh],
Pant, S.[Sudarshan],
Kim, S.H.[Soo-Hyung],
Lee, G.S.[Guee-Sang],
Oh, I.J.[In-Jae],
Kang, S.R.[Sae-Ryung],
Survival Analysis based on Lung Tumor Segmentation using Global
Context-aware Transformer in Multimodality,
ICPR22(5162-5169)
IEEE DOI
2212
Measurement, Image segmentation, Analytical models, Lung cancer,
Predictive models, Transformers, Feature extraction,
Medical image analysis
BibRef
Li, C.Y.[Chun-Yuan],
Zhu, X.L.[Xin-Liang],
Yao, J.W.[Jia-Wen],
Huang, J.Z.[Jun-Zhou],
Hierarchical Transformer for Survival Prediction Using Multimodality
Whole Slide Images and Genomics,
ICPR22(4256-4262)
IEEE DOI
2212
Pathology, Genomics, Imaging, Computer architecture,
Feature extraction, Transformers, Bioinformatics
BibRef
Zhu, X.L.[Xin-Liang],
Yao, J.W.[Jia-Wen],
Zhu, F.Y.[Fei-Yun],
Huang, J.Z.[Jun-Zhou],
WSISA: Making Survival Prediction from Whole Slide Histopathological
Images,
CVPR17(6855-6863)
IEEE DOI
1711
Cancer, Computational modeling, Feature extraction, Lungs, Training, Tumors
BibRef
Shakur, A.H.[Ameer Hamza],
Qian, X.N.[Xiao-Ning],
Wang, Z.Y.[Zhang-Yang],
Mortazavi, B.[Bobak],
Huang, S.[Shuai],
GPSRL: Learning Semi-Parametric Bayesian Survival Rule Lists from
Heterogeneous Patient Data,
ICPR21(10608-10615)
IEEE DOI
2105
Performance evaluation, Computational modeling, Sociology,
Gaussian processes, Data models, Bayes methods, Sensors
BibRef
Chen, R.J.[Richard J.],
Lu, M.Y.[Ming Y.],
Weng, W.H.[Wei-Hung],
Chen, T.Y.[Tiffany Y.],
Williamson, D.F.[Drew FK.],
Manz, T.[Trevor],
Shady, M.[Maha],
Mahmood, F.[Faisal],
Multimodal Co-Attention Transformer for Survival Prediction in
Gigapixel Whole Slide Images,
ICCV21(3995-4005)
IEEE DOI
2203
Representation learning, Visualization, Histopathology, Genomics,
Predictive models, Transformers, Biological information theory,
Vision + other modalities
BibRef
Nuechterlein, N.[Nicholas],
Li, B.[Beibin],
Seyfioglu, M.S.[Mehmet Saygin],
Mehta, S.[Sachin],
Cimino, P.J.[Patrick J.],
Shapiro, L.[Linda],
Leveraging Unlabeled Data for Glioma Molecular Subtype and Survival
Prediction,
ICPR21(7149-7156)
IEEE DOI
2105
Training, Genomics, Imaging, Predictive models, Brain modeling,
Data models, Bioinformatics
BibRef
Liu, Z.,
Sun, Q.,
Bai, H.,
Liang, C.,
Chen, Y.,
Li, Z.,
3D Deep Attention Network for Survival Prediction from Magnetic
Resonance Images in Glioblastoma,
ICIP19(1381-1384)
IEEE DOI
1910
Deep learning, Attention mechanism, Survival analysis,
Magnetic resonance image, Glioblastoma
BibRef
Zhou, M.[Mu],
Hall, L.O.[Lawrence O.],
Goldgof, D.B.[Dmitry B.],
Exploring Brain Tumor Heterogeneity for Survival Time Prediction,
ICPR14(580-585)
IEEE DOI
1412
Accuracy
BibRef
Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Anotomical Landmark Detection, Landmark Location in Various Sensors .