21.1.2 Survival Analysis, Cancer Survival

Chapter Contents (Back)
Medical, Applications. Application, Medical. Diagnosis. Survival. Each one fits in another topic, but the general theme is survival analysis.
See also Brain Tumors, Cortex, Cancer.

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.[GŁnter], 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.[Junyong], Sun, L.[Liang], Lu, Z.X.[Zi-Xiao], Sheng, J.P.[Jian-Peng], Zhu, Q.[Qi], Zhang, D.[Daoqiang],
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.[Mengkang], Li, R.[Ruijiang], 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.[Daoqiang],
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


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 .


Last update:Apr 10, 2024 at 09:54:40