Quantifying Explainers of Graph Neural Networks in Computational Pathology |
Guillaume Jaume, Pushpak Pati, Behzad Bozorgtabar, Antonio Foncubierta-Rodríguez, Florinda Feroce, Anna Maria Anniciello, Tilman Rau, Jean-Philippe Thiran, Maria Gabrani, Orcun Goksel |
Computer Vision and Pattern Recognition (CVPR) |
2021 |
Digital Pathology |
Modeling uncertainty in multimodal fusion for lung cancer survival analysis |
H. Wang. V. Subramanian, T. Syeda-Mahmood |
IEEE 18th International Symposium on Biomedical Imaging (ISBI) |
2021 |
Chest Xray |
Multimodal Fusion of Imaging and Genomics for Lung Cancer Recurrence Prediction |
Vaishnavi Subramanian, Minh N. Do, Tanveer Syeda-Mahmood |
IEEE 17th International Symposium on Biomedical Imaging (ISBI) |
2020 |
Lung Cancer |
Mitosis Detection Under Limited Annotation: A Joint Learning Approach |
Pushpak Pati, Antonio Foncubierta-Rodríguez, Orçun Göksel, Maria Gabrani |
IEEE 17th International Symposium on Biomedical Imaging (ISBI) |
2020 |
Digital Pathology |
HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification |
Pushpak Pati, Guillaume Jaume, Lauren Alisha Fernandes, Antonio Foncubierta-Rodríguez, Florinda Feroce, Anna Maria Anniciello, Giosue Scognamiglio, Nadia Brancati, Daniel Riccio, Maurizio Di Bonito, Giuseppe De Pietro, Gerardo Botti, Orcun Goksel, Jean-Philippe Thiran, Maria Frucci, Maria Gabrani |
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis (UNSURE/GRAIL@MICCAI) |
2020 |
Digital Pathology |
Combining Deep Learning and Knowledge-driven Reasoning for Chest X-Ray Findings Detection |
Ashutosh Jadhav, Ken C. L. Wong, Joy T. Wu, Mehdi Moradi, Tanveer F. Syeda-Mahmood |
American Medical Informatics Association (AMIA) Annual Symposium |
2020 |
Chest Xray |
Looking in the Right Place for Anomalies: Explainable Ai Through Automatic Location Learning |
Satyananda Kashyap, Alexandros Karargyris, Joy T. Wu, Yaniv Gur, Arjun Sharma, Ken C. L. Wong, Mehdi Moradi, Tanveer F. Syeda-Mahmood |
IEEE 17th International Symposium on Biomedical Imaging (ISBI) |
2020 |
Lung Cancer |
Multimodal fusion using sparse CCA for breast cancer survival prediction |
V. Subramanian, M. Do, T. Syeda-Mahmood |
IEEE 18th International Symposium on Biomedical Imaging (ISBI) |
2021 |
Breast Cancer |
Creation and validation of a chest X-ray dataset with eye-tracking and report dictation for AI development |
Alexandros Karargyris, Satyananda Kashyap, Ismini Lourentzou, Joy t. Wu, Arjun Sharma, Matthew Tong, Shafiq abedin, David Beymer, Vandana Mukherjee, Elizabeth a. Krupinski ,Mehdi Moradi |
Scientific Data Journal |
2021 |
Chest Xray |
A deep community based approach for large scale content based X-ray image retrieval |
Nandinee Fariah Haq , Mehdi Moradi, Z Jane Wang |
Medical Image Analysis Journal |
2021 |
Chest Xray |
Reducing annotation effort in digital pathology: A Co-Representation learning framework for classification tasks |
PushpakPati, AntonioFoncubierta-Rodríguez, OrcunGoksel, MariaGabrani |
Medical Image Analysis |
2021 |
Digital Pathology |
On the Role of Artificial Intelligence in Medical Imaging of COVID-19 |
Jannis Born, David Beymer, Deepta Rajan, Adam Coy, Vandana V. Mukherjee, Matteo Manica, Prasanth Prasanna, Deddeh Ballah, Michal Guindy, Dorith Shaham, Pallav L. Shah, Emmanouil Karteris, Jan L. Robertus, Maria Gabrani, Michal Rosen-Zvi |
Patterns Journal |
2021 |
COVID-19 |
Improving the Performance and Explainability of Mammogram Classifiers with Local Annotations |
L. Ness, E.Barkan, M.Ozery-Flato |
iMIMIC Workshop, Medical Image Computing and Computer Assisted Intervention (MICCAI) |
2020 |
Breast Imaging |
Multi-task learning for detection and classification of cancer in screening mammography |
Maria V. Sainz de Cea, Karl Diedrich, Ran Bakalo, Lior Ness, David Richmond |
Medical Image Computing and Computer Assisted Intervention (MICCAI) |
2020 |
Breast Imaging |
Radiomics for predicting response to neoadjuvant chemotherapy treatment in breast cancer |
Simona Rabinovici-Cohen, Tal Tlusty, Ami Abutbul, Kari Antila, Xosé Fernandez, Beatriz Grandal Rejo, Efrat Hexter, Oliver Hijano Cubelos, Abed Khateeb, Juha Pajula, Shaked Perek |
Proceedings of SPIE 11318 Breast Imaging, Houston, Texas, United States, 2020 |
2020 |
Breast Imaging |
Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms |
Thomas Schaffter, Diana SM Buist, Christoph I Lee, Yaroslav Nikulin, Dezso Ribli, Yuanfang Guan, William Lotter, Zequn Jie, Hao Du, Sijia Wang, Jiashi Feng, Mengling Feng, Hyo-Eun Kim, Francisco Albiol, Alberto Albiol, Stephen Morrell, Zbigniew Wojna, Mehmet Eren Ahsen, Umar Asif, Antonio Jimeno Yepes, Shivanthan Yohanandan, Simona Rabinovici-Cohen, Darvin Yi, Bruce Hoff, Thomas Yu, Elias Chaibub Neto, Daniel L Rubin, Peter Lindholm, Laurie R Margolies, Russell Bailey McBride, Joseph H Rothstein, Weiva Sieh, Rami Ben-Ari, Stefan Harrer, Andrew Trister, Stephen Friend, Thea Norman, Berkman Sahiner, Fredrik Strand, Justin Guinney, Gustavo Stolovitzky, Lester Mackey, Joyce Cahoon, Li Shen, Jae Ho Sohn, Hari Trivedi, Yiqiu Shen, Ljubomir Buturovic, Jose Costa Pereira, Jaime S Cardoso, Eduardo Castro, Karl Trygve Kalleberg, Obioma Pelka, Imane Nedjar, Krzysztof J Geras, Felix Nensa, Ethan Goan, Sven Koitka, Luis Caballero, David D Cox, Pavitra Krishnaswamy, Gaurav Pandey, Christoph M Friedrich, Dimitri Perrin, Clinton Fookes, Bibo Shi, Gerard Cardoso Negrie, Michael Kawczynski, Kyunghyun Cho, Can Son Khoo, Joseph Y Lo, A Gregory Sorensen, Hwejin Jung |
Journal of the American Medical Association (JAMA) Network Open, 2020 |
2020 |
Breast Imaging |
Multimodal Prediction of Breast Cancer Relapse Prior to Neoadjuvant Chemotherapy Treatment |
Simona Rabinovici-Cohen, Ami Abutbul, Xosé Fernandez, Oliver Hijano Cubelos, Shaked Perek, Tal Tlusty |
PRIME-MICCAI Workshop, 2020 |
2020 |
Breast Imaging |
The case of missed cancers: Applying AI as a radiologist’s safety net |
Michal Chorev,Yoel Shoshan, Adam Spiro, Shaked Naor, Alon Hazan, Vesna Barros, Iuliana Weinstein, Esma Herzel, Varda Shalev, Michal Guindy,Michal Rosen-Zvi |
Medical Image Computing and Computer Assisted Intervention (MICCAI) |
2020 |
Breast Imaging, Machine Learning |
DDxNet: a deep learning model for automatic interpretation of electronic health records, electrocardiograms and electroencephalograms |
Deepta Rajan, Jayaraman J. Thiagarajan, Sameeksha Katoch and Andreas Spanias |
Nature Scientific Reports |
2020 |
Deep Learning for Clinical Time-Series |
Automatic localization of lung opacity in chest CT images - A Real-world Study |
Yiting Xie, Deepta Rajan, Larissa Schudlo et al. |
SPIE |
2020 |
Segmentation of Lung Opacities in CT |
Self-Training with Improved Regularization for Sample-Efficient Chest X-Ray Classification |
Deepta Rajan, Jayaraman J. Thiagarajan, Alexandros Karargyris, Satyananda Kashyap |
SPIE |
2020 |
Semi-Supervised Learning for Chest X-rays |
Improving Reliability of Clinical Models using Prediction Calibration |
Jayaraman J Thiagarajan, Bindya Venkatesh, Deepta Rajan, Prasanna Sattigeri |
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis (UNSURE/GRAIL@MICCAI) |
2020 |
Interpretability and Uncertainty Quantification |
Automatic Diagnosis of Pulmonary Embolism Using an Attention-guided Framework: A Large-scale Study |
Luyao Shi, Deepta Rajan, Shafiq Abedin, Manikanta Srikar Yellapragada, David Beymer, Ehsan Dehghan |
MIDL (PMLR) |
2020 |
Attention-guided Segmentation for Cardiac CT |
Pi-PE: A pipeline for pulmonary embolism detection using sparsely annotated 3D CT images |
Deepta Rajan, David Beymer, Shafiqul Abedin, Ehsan Dehghan |
NeurIPS ML4H (PMLR) |
2019 |
Multiple Instance Learning with Weak Annotations for Cardiac CT |
Leveraging medical visual question answering with supporting facts |
Tomasz Kornuta, Deepta Rajan, Chaitanya Shivade, Alexis Asseman, Ahmet S Ozcan |
CLEF (Working Notes) |
2019 |
Medical VQA for Radiology |
Understanding Behavior of Clinical Models under Domain Shifts |
Jayaraman J. Thiagarajan, Deepta Rajan, Prasanna Sattigeri |
KDD Data Science for Health |
2019 (Best Paper) |
Domain Adaptation in Electronic Health Records Analysis |
Generalization studies of neural network models for cardiac disease detection using limited channel ecg |
Deepta Rajan, David Beyner, Girish Narayanan |
IEEE CinC |
2018 |
ECG Cardiac Disease Detection |
A generative modeling approach to limited channel ECG classification |
Deepta Rajan et al. |
IEEE EMBC |
2018 |
ECG Abnormality Detection |
Attend and diagnose: Clinical time series analysis using attention models |
Deepta Rajan et al. |
AAAI |
2018 |
Clinical Time-Series (Electronic Health Records) |
Predicting Breast Cancer by Applying Deep Learning to Linked Health Records and Mammograms |
Akselrod-Ballin, Ayelet; Chorev, Michal; Shoshan, Yoel; Spiro, Adam; Hazan, Alon; Melamed, Roie; Barkan, Ella; Herzel, Esma; Naor, Shaked; Karavani, Ehud; Koren, Gideon; Goldschmidt, Yaara; Shalev, Varda; Rosen-Zvi, Michal; Guindy, Michal |
Radiology |
2019 |
Breast Imaging |
Deep Learning for Automatic Detection of Abnormal Findings in Breast Mammography |
Akselrod-Ballin, Ayelet; Karlinsky, Leonid.; Hazan, Alon; Bakalo, Ran; Horesh, Ami Ben; Shoshan, Yoel; Barkan, Ella |
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support |
2017 |
Breast Imaging |
Semi-supervised learning with generative adversarial networks for chest X-ray classification with ability of data domain adaptation |
Madani, A.; Moradi, M.; Karargyris, A.; Syeda-Mahmood, T. |
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) |
2018 |
Chest Xray |
Hybrid Mass Detection in Breast MRI Combining Unsupervised Saliency Analysis and Deep Learning |
Amit, Guy; Hadad, Omer; Alpert, Sharon; Tlusty, Tal; Gur, Yaniv; Ben-Ari, Rami; Hashoul, Sharbell |
Medical Image Computing and Computer Assisted Intervention (MICCAI 2017), Springer International Publishing |
2017 |
Breast Imaging |
Mammogram Classification with Ordered Loss |
Ben-Ari, Rami; Shoshan, Yoel; Tlusty, Tal |
Artificial Intelligence in Medicine, Springer International Publishing |
2019 |
Breast Imaging |
Mammogram Classification and Abnormality Detection from Nonlocal Labels using Deep Multiple Instance Neural Network |
Choukroun, Yoni; Bakalo, Ran; Ben-Ari, Rami; Akselrod-Ballin, Ayelet; Barkan, Ella; Kisilev, Pavel |
Eurographics Workshop on Visual Computing for Biology and Medicine |
2017 |
Breast Imaging |
A deep learning framework for context-aware mitotic activity estimation in whole slide images |
Pushpak Pati; Raul Catena; Orcun Goksel; Maria Gabrani |
SPIE Medical Imaging |
2019 |
Digital Pathology |
A Fast and Scalable Pipeline for Stain Normalization of Whole-Slide Images in Histopathology | springerprofessional.de |
Milos Stanisavljevic, Andreea Anghel, Nikolaos Papandreou, Sonali Andani, Pushpak Pati, Jan Hendrik Ruschoff, Peter Wild, Maria Gabrani, Haralampos Pozidis |
ECCV 2018 Workshops |
2018 |
Digital Pathology |
Medical sieve: a cognitive assistant for radiologists and cardiologists |
Syeda-Mahmood, T.; Walach, E.; Beymer, D.; Gilboa-Solomon, F.; Moradi, M.; Kisilev, P.; Kakrania, D.; Compas, C.; Wang, H.; Negahdar, R.; Cao, Y.; Baldwin, T.; Guo, Y.; Gur, Y.; Rajan, D.; Zlotnick, A.; Rabinovici-Cohen, S.; Ben-Ari, R.; Guy, Amit; Prasanna, P.; Morey, J.; Boyko, O.; Hashoul, S. |
Medical Imaging 2016: Computer-Aided Diagnosis |
2016 |
Cardiac/Chest/Brain/General Medical Imaging |
A Structure-Aware Convolutional Neural Network for Skin Lesion Classification |
Thandiackal, Kevin; Goksel, Orcun |
OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis |
2018 |
Dermatology Imaging |
Maximizing AUC with Deep Learning for Classification of Imbalanced Mammogram Datasets |
Sulam, J.; Ben-Ari, R.; Kisilev, P. |
Proceedings of the Eurographics Workshop on Visual Computing for Biology and Medicine |
2017 |
Breast Imaging |
Deep positive-unlabeled learning for region of interest localization in breast tissue images |
Pati, Pushpak; Andani, Sonali; Pediaditis, Matthew; Viana, Matheus Palhares; Rüschoff, Jan Hendrik; Wild, Peter; Gabrani, Maria |
Medical Imaging 2018: Digital Pathology |
2018 |
Breast Imaging |
A feature agnostic approach for glaucoma detection in OCT volumes |
Maetschke, Stefan; Antony, Bhavna; Ishikawa, Hiroshi; Wollstein, Gadi; Schuman, Joel; Garnavi, Rahil |
PLOS ONE |
2019 |
Retinal Imaging |
Retinal optical coherence tomography image enhancement via deep learning |
Halupka, Kerry J.; Antony, Bhavna J.; Lee, Matthew H.; Lucy, Katie A.; Rai, Ravneet S.; Ishikawa, Hiroshi; Wollstein, Gadi; Schuman, Joel S.; Garnavi, Rahil |
Biomedical optics express, vol 9 issue 12 pages 6205-6221 |
2018 |
Retinal Imaging |
Joint Segmentation and Uncertainty Visualization of Retinal Layers in Optical Coherence Tomography Images Using Bayesian Deep Learning |
Sedai, Suman; Antony, Bhavna; Mahapatra, Dwarikanath; Garnavi, Rahil |
Computational Pathology and Ophthalmic Medical Image Analysis, Springer International Publishing |
2018 |
Retinal Imaging |
Deep Multiscale Convolutional Feature Learning for Weakly Supervised Localization of Chest Pathologies in X-ray Images |
Sedai, Suman; Mahapatra, Dwarikanath; Ge, Zongyuan; Chakravorty, Rajib; Garnavi, Rahil |
Machine Learning in Medical Imaging, Springer International Publishing |
2018 |
Chest Xray |
Deformable medical image registration using generative adversarial networks |
Mahapatra, D.; Antony, B.; Sedai, S.; Garnavi, R. |
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) |
2018 |
Retinal and Cardiac |
Deep Semantic Instance Segmentation of Tree-Like Structures Using Synthetic Data |
Halupka, K.; Garnavi, R.; Moore, S. |
2019 IEEE Winter Conference on Applications of Computer Vision (WACV) |
2019 |
Cardiac/Chest/Brain/General Medical Imaging |
Building a Benchmark Dataset and Classifiers for Sentence-Level Findings in AP Chest X-rays |
Syeda-Mahmood, Tanveer; Ahmad, Hassan M.; Ansari, Nadeem; Gur, Yaniv; Kashyap, Satyananda; Karargyris, Alexandros; Moradi, Mehdi; Pillai, Anup; Sheshadri, Karthik; Wang, Weiting; Wong, Ken C. L.; Wu, Joy T. |
arXiv:1906.09336 [cs], IEEE 16th International Symposium on Biomedical Imaging (ISBI) |
2019 |
Chest Xray |
Boosting the rule-out accuracy of deep disease detection using class weight modifiers |
Karargyris, Alexandros; Wong, Ken C. L.; Wu, Joy T.; Moradi, Mehdi; Syeda-Mahmood, Tanveer |
arXiv:1906.09354 [cs, eess], IEEE 16th International Symposium on Biomedical Imaging (ISBI) |
2019 |
Chest Xray |
Artificial intelligence for point of care radiograph quality assessment |
Kashyap, Satyananda; Moradi, Mehdi; Karargyris, Alexandros; Wu, Joy T.; Morris, Michael; Saboury, Babak; Siegel, Eliot; Syeda-Mahmood, Tanveer |
Medical Imaging 2019: Computer-Aided Diagnosis |
2019 |
Chest Xray |
Age prediction using a large chest x-ray dataset |
Karargyris, A.; Kashyap, S.; Wu, J. T.; Sharma, A.; Moradi, M.; Syeda-Mahmood, T. |
Medical Imaging 2019: Computer-Aided Diagnosis |
2019 |
Chest Xray |
Disease Detection in Weakly Annotated Volumetric Medical Images using a Convolutional LSTM Network |
Braman, Nathaniel; Beymer, David; Dehghan, Ehsan |
arXiv:1812.01087 [cs], Medical Imaging Meets NeurIPS Workshop at NeurIPS 2018 |
2018 |
Lung Imaging |
Lung tissue characterization for emphysema differential diagnosis using deep convolutional neural networks |
Negahdar, Mohammadreza; Beymer, David |
Medical Imaging 2019: Computer-Aided Diagnosis |
2019 |
Lung Imaging |
Automated Detection and Type Classification of Central Venous Catheters in Chest X-Rays |
Subramanian, Vaishnavi; Wang, Hongzhi; Wu, Joy T.; Wong, Ken C. L.; Sharma, Arjun; Syeda-Mahmood, Tanveer |
arXiv:1907.01656 [cs, eess], Medical Image Computing and Computer Assisted Intervention (MICCAI) |
2019 |
Chest Xray |
Uncertainty guided semisupervised segmentation of retinal layers in OCT images |
Suman Sedai, Bhavna Antony, Hiroshi Ishikawa, Joel Schuman, Gadi Wollstein, Ravneet Rai, Katie Jones, Rahil Garnavi |
Medical Image Computing and Computer Assisted Intervention (MICCAI) |
2019 |
Retinal Imaging |
SegNAS3D: Network Architecture Search with Derivative-Free Global Optimization for 3D Image Segmentation |
Ken CL Wong, Mehdi Moradi |
Medical Image Computing and Computer Assisted Intervention (MICCAI) |
2019 |
Cardiac/Chest/Brain/General Medical Imaging |
Deep Network Anatomy Segmentation with Limited Annotations Using Auxiliary Labels |
Ahmed Harouni, Hongzhi Wang, Tanveer Syeda-Mahmood, David Beymer |
IEEE 16th International Symposium on Biomedical Imaging (ISBI) |
2019 |
Cardiac/Chest/Brain/General Medical Imaging |
Learning from Longitudinal Mammography Studies |
Perek S, Ness L, Amit M, Barkan E, Amit G |
Medical Image Computing and Computer Assisted Intervention (MICCAI) |
2019 |
Breast Imaging |
Accelerated ML-assisted Tumor Detection in High-Resolution Histopathology Images |
Ioannou N, Stanisavljevic M, Anghel A, Papandreou N, Andani S, Hendrick Ruschoff J, Wild P, Gabrani M, Pozidis H |
Medical Image Computing and Computer Assisted Intervention (MICCAI) |
2019 |
Digital Pathology |
Mammography Dual View Mass Correspondence |
S. Perek, A. Hazan, E. Barkan, A. Akselrod Ballin |
KDD |
2018 |
Breast Imaging |
Unsupervised clustering of mammograms for outlier detection and breast density estimation |
R. Ben-Ari, T. Tlusty and G.Amit |
ICPR |
2018 |
Breast Imaging |
A CNN based method for Automated Mass Detection and classification in Mammograms |
Akselrod-Ballin, A., Karlinsky, L., Alpert, S., Hasoul, S., Ben-Ari, R. and Barkan, E. |
Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization |
2017 |
Breast Imaging |
Mammogram Classification and Abnormality Detection from Nonlocal Labels using Deep Multiple Instance Neural Networks |
Y. Choukroun, R. Bakalo, R. Ben-Ari, A. Akselrod-Ballin, E. Barkan and P. Kisilev |
EG VCBM |
2017 |
Breast Imaging |
Weakly Supervised DNN with AUC Loss for Classification of Imbalanced Mammogram Datasets |
J. Sulam, R. Ben-Ari and P. Kisilev |
EG VCBM |
2017 |
Breast Imaging |
Hybrid Mass Detection in Breast MRI combining Unsupervised Saliency Analysis and Deep Learning |
G. Amit, O.Hadad, S.Alpert, T. Tlusty, Y. Gur, R. Ben-Ari, S.Hashoul |
Medical Image Computing and Computer Assisted Intervention (MICCAI) |
2017 |
Breast Imaging |
Deep Learning for Automatic Detection of Abnormal Findings in Breast Mammography |
Akselrod-Ballin, A., Karlinsky, L., Alpert, S., Hasoul, S., Ben-Ari, R. and Barkan, E. |
Medical Image Computing and Computer Assisted Intervention (MICCAI) - DLMIA |
2017 |
Breast Imaging |
Domain Specific CNN’s for Detection of Architectural distortion in Mammograms |
R. Ben-Ari, A. Akselrod-Ballin, L. Karlinsky, S. Hashoul |
IEEE 14th International Symposium on Biomedical Imaging (ISBI) |
2017 |
Breast Imaging |
Classification of Breast Lesion using Cross-Modal Deep Learning |
O. Hadad, R. Bakalo, R. Ben-Ari, S. Hashoul, G. Amit |
IEEE 14th International Symposium on Biomedical Imaging (ISBI) |
2017 |
Breast Imaging |
Classification of Breast MRI Lesions using Small-Size Training Sets: Comparison of Deep Learning Approaches. |
G. Amit, R. Ben-Ari , O. Hadada , E. Monovicha , N. Granot , S. Hashoul |
SPIE |
2017 |
Breast Imaging |
Medical image description using multi-task-loss CNN |
P. Kisilev, E. Sason, S. Hashoul, E. Barkan, E. Walach |
Medical Image Computing and Computer Assisted Intervention (MICCAI) DLMIA |
2016 |
Breast Imaging |
A Region Based Convolutional Network for Tumor Detection and Classification in Breast Mammography |
Akselrod-Ballin, A., Karlinsky, L., Alpert, S., Hasoul, S., Ben-Ari, R. and Barkan, E |
Medical Image Computing and Computer Assisted Intervention (MICCAI) DLMIA |
2016 |
Breast Imaging |
Semantic Object Boundary Detection Using Convolutional Neural Networks with Regression Output |
P. Kisilev and E. Sason |
IBM Deep Learning Workshop |
2016 |
Breast Imaging |
Computational mammography using deep neural networks |
Dubrovina, A., Kisilev, P., Ginsburg, B., Hashoul, S. and Kimmel, R. |
Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, pp.1-5. |
2016 |
Breast Imaging |
Deformable image registration using generative adversial networks |
Dwarkanath Mahapatra, Bhavna Antony, Suman Sedai, Rahil Garnavi |
IEEE 15th International Symposium on Biomedical Imaging (ISBI) |
2018 |
Retinal Imaging |
Semi-supervised Segmentation of Optic Cup in Retinal Fundus Images Using Aariational Autoencoder |
S. Sedai, D. Mahapatra, R. Garnavi, |
Medical Image Computing and Computer Assisted Intervention (MICCAI) |
2017 |
Retinal Imaging |
Image Super Resolution Using Generative Adversarial Networks and Local Saliency Maps for Retinal Image Analysis |
D. Mahapatra, B. Bozorgtabar, R. Garnavi |
Medical Image Computing and Computer Assisted Intervention (MICCAI) |
2017 |
Retinal Imaging |
A novel hybrid approach for severity assessment of diabetic retinopathy in colour fundus images |
P. Roy, R. Tennakoon, K. Cao, S. Sedai, D. Mahapatra, S. Maetschke, and R. Garnavi |
IEEE 14th International Symposium on Biomedical Imaging (ISBI) |
2017 |
Retinal Imaging |
Multi-Stage Segmentation of the Fovea in Retinal Fundus Images Using Fully Convolutional Neural Networks |
Suman Sedai, Ruwan Tennakoon, Pallab Roy, Khoa Cao, Rahil Garnavi |
IEEE 14th International Symposium on Biomedical Imaging (ISBI) |
2017 |
Retinal Imaging |
Retinal Image Quality Classification Using Saliency Maps And CNNs |
Mahapatra, D., Roy, P.K., Sedai, S. and Garnavi, R. |
In International Workshop on Machine Learning in Medical Imaging (pp. 172-179). Springer International Publishing. |
2016 |
Retinal Imaging |
Quality Classification for DR Screening Using Convolutional Neural Networks |
R. Tennakoon, D. Mahapatra, R.Garnavi |
Medical Image Computing and Computer Assisted Intervention (MICCAI) - OMIA |
2016 |
Retinal Imaging |
Automatic Eye Type Detection in Retinal Fundus Image Using Fusion of Transfer Learning and Anatomical Features. |
Roy, P.K., Chakravorty, R., Sedai, S., Mahapatra, D. and Garnavi, R. |
In Digital Image Computing: Techniques and Applications (DICTA), 2016 International Conference on (pp. 1-7). IEEE. |
2016 |
Retinal Imaging |
GraspNet: an efficient convolutional neural network for real-time grasp detection for low powered devices |
U. Asif, J. Tang, S. Harrer |
accepted full paper at IJCAI |
2018 |
Epilepsy (and Neurobionics) EEG |
A portable low-cost EEG motor imagery-based brain-computer interface |
S. Yohanandan, I. Kiral-Kornek, B. Mashford, J. Tang, U. Asif, S. Harrer |
accepted full paper at IEEE Engineering in Medicine and Biology Conference (EMBC) |
2018 |
Epilepsy (and Neurobionics) EEG |
Deep learning enabled automatic abnormal EEG identification |
S. Roy, I. Kiral-Kornek, S. Harrer |
accepted full paper at IEEE Engineering in Medicine and Biology Conference (EMBC) |
2018 |
Epilepsy (and Neurobionics) EEG |
ChronoNet: A deep recurrent neural network for abnormal EEG identification |
S. Roy, I. Kiral-Kornek, S. Harrer |
arXiv |
2018 |
Epilepsy (and Neurobionics) EEG |
A mobile and tunable seizure prediction system using deep-learning |
Isabell Kiral-Kornek, Subhrajit Roy, Ewan Nurse, Benjamin S. Mashford, Philippa Karoly, Daniel Payne, Susmita Saha, Terence O’Brien, David B. Grayden, Mark Cook, Dean Freestone, Stefan Harrer |
in Proceedings of the Annual Meeting of the American Epilepsy Society |
2017 |
Epilepsy (and Neurobionics) EEG |
Epileptic seizure prediction using big data and deep learning: toward a mobile system |
Isabell Kiral-Kornek, Subhrajit Roy, Ewan Nurse, Benjamin S. Mashford, Philippa Karoly, Thomas Carrol, Daniel Payne, Susmita Saha, Steven Baldassano, Terence O’Brien, David B. Grayden, Mark Cook, Dean Freestone, Stefan Harrer |
EBioMedicine (a Lancet journal) |
2017 |
Epilepsy (and Neurobionics) EEG |
Improving classification accuracy of feedforward neural networks for spiking neuromorphic chips |
Jimeno Yepes, A., Tang, J., Mashford, B.S. |
IJCAI |
2017 |
Epilepsy (and Neurobionics) EEG |
Deep-learning-based analysis of EEG-data for developing real-time brain-machine interfaces |
B. S. Mashford, A. Jimeno Yepes, I. Kiral-Kornek, J. Tang. S. Harrer |
IBM Journal of Research and Development |
2017 |
Epilepsy (and Neurobionics) EEG |
TrueNorth-enabled Real-time Classification of EEG Data for Brain-Computer Interfacing |
I. Kiral-Kornek, D. Mendis, E, S. Nurse, B. S. Mashford, D, R. Freestone, D, B. Grayden, S. Harrer |
In Proceedings of the IEEE Engineering in Medicine and Biology Conference (EMBC) |
2017 |
Epilepsy (and Neurobionics) EEG |
Decoding EEG and LFP signals using deep learning: heading TrueNorth |
Nurse, E., Mashford, B.S., Yepes, A.J., Kiral-Kornek, I., Tang, J., Deligianni, L., Grayden, D. Freestone, D.R, and Harrer, S. |
In Proceedings of the ACM International Conference on Computing Frontiers (ACM), (pp. 259-266) |
2016 |
Epilepsy (and Neurobionics) EEG |
From Wearables to THINKables - Deep Learning, Nano-biosensors and the Next Generation of Mobile Devices |
Harrer, S., Kiral-Kornek, I., Kerr, R., Mashford, B., Tang, J., Yepes, A.J. and Deligianni, H., 2016 |
White Paper, ICONN |
2016 |
Epilepsy (and Neurobionics) EEG |
Role of big data and machine learning in diagnostic decision support in radiology |
T. Syeda-Mahmood |
in Journal of American College of Radiology, Volume 15, Issue 3, Part B, Pages 569–576 |
2018 |
Cardiac/Chest/Brain/General Medical Imaging |
Echocardiagraphy segmentation based on a shape-guided deformable model driven by a fully convolutional network prior |
Gopalkrishna Veni, Mehdi Moradi, Hakan Bulu, Girish Narayan, Tanveer Syeda-Mahmood |
IEEE 15th International Symposium on Biomedical Imaging (ISBI) |
2018 |
Cardiac/Chest/Brain/General Medical Imaging |
Detecting Anomalies from Echocardiography using Multi-View Regression of Clinical Measurements |
Allen Lu, Ehsan Dehghan, Mehdi Moradi, Tanveer Syeda-Mahmood |
IEEE 15th International Symposium on Biomedical Imaging (ISBI) |
2018 |
Cardiac/Chest/Brain/General Medical Imaging |
Semi-supervised learning with generative adversarial networks for chest X-ray classification with ability of data domain adaptation |
Ali Madani, Mehdi Moradi, Alexandros Karargyris, Tanveer Syeda-Mahmood |
IEEE 15th International Symposium on Biomedical Imaging (ISBI) |
2018 |
Cardiac/Chest/Brain/General Medical Imaging |
Universtal multimodal deep network for classification and segmentation of medical images |
Ahmed Harouni, Alexandros Karargyris, Mohammadreza Negahdar, David Beymer, Tanveer Syeda- Mahmood |
IEEE 15th International Symposium on Biomedical Imaging (ISBI) |
2018 |
Cardiac/Chest/Brain/General Medical Imaging |
Rapid annotation of 3D medical imaging datasets using registration-based interpolation and adaptive slice selection |
Honzhi Wang, Prasanth Prasanna, M.D., Tanveer Syeda-Mahmood |
IEEE 15th International Symposium on Biomedical Imaging (ISBI) |
2018 |
Cardiac/Chest/Brain/General Medical Imaging |
Segmentation of anatomical structures in cardiac CTA using multi-label V-Net |
Hui Tang, Mehdi Moradi, Ahmed El harouni, Hongzhi Wang, Prasanth Prasanna, Gopal Veni, Tanveer Syeda-Mahmood |
SPIE |
2018 |
Cardiac/Chest/Brain/General Medical Imaging |
Chest x-ray generation and data augmentation for cardiovascular abnormality classification |
Ali Madani, Tanveer Syeda-Mahmood, Alexandros Karargyris |
SPIE |
2018 |
Cardiac/Chest/Brain/General Medical Imaging |
Building Disease Detection Algorithms with Very Small Number of Positive Samples |
C. L. (Ken) Wong, A. Karargyris, T. Syeda-Mahmood, M. Moradi |
Medical Image Computing and Computer Assisted Intervention (MICCAI) |
2017 |
Cardiac/Chest/Brain/General Medical Imaging |
A multi-atlas approach to region of interest detection for medical image classification |
H. Wang, M. Moradi, Y. Gur, P. Prasanna, T. Syeda-Mahmood |
Medical Image Computing and Computer Assisted Intervention (MICCAI) |
2017 |
Cardiac/Chest/Brain/General Medical Imaging |
Towards An Efficient Way of Building Annotated Medical Image Collections for Big Data Studies |
Y. Gur, M. Moradi, H. Bulu, Y. Guo, C. Compas, T. Syeda-Mahmood |
LABELS |
2017 |
Cardiac/Chest/Brain/General Medical Imaging |
Automatic Labeling of Continuous Wave Doppler Images Based on Combined Image and Sentence Networks |
M. Moradi, Y. Guo, Y. Gur, T. Syeada-Mahmood |
IEEE 14th International Symposium on Biomedical Imaging (ISBI) |
2017 |
Cardiac/Chest/Brain/General Medical Imaging |
A Cross-Modality Neural Network Transform for Semi-Automatic Medical Image Annotation |
M. Moradi, Y. Guo, Y. Gur, M. Negahdar, and T. Syeda-Mahmood |
Medical Image Computing and Computer Assisted Intervention (MICCAI) |
2016 |
Cardiac/Chest/Brain/General Medical Imaging |
A hybrid learning approach for semantic labeling of cardiac CT slices and recognition of body position |
Mehdi Moradi, Yaniv Gur, Hongzhi Wang, Prasanth Prasanna, Tanveer Syeda-Mahmood |
IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 1418--1421 |
2016 |
Cardiac/Chest/Brain/General Medical Imaging |
Skin Disease Recognition Using Deep Saliency Features and Multimodal Learning of Dermoscopy and Clinical Images |
Z. Ge, R. Chakravorty, A. Bowling R. Garnavi |
Medical Image Computing and Computer Assisted Intervention (MICCAI) |
2017. |
Dermatology Imaging |
Exploiting Local and Generic Features for Skin Lesions Classification |
Z.Ge, S.Demyanov, S.Bozorgtabar, R. Chakravorty, A. Bowling, R. Garnavi. |
IEEE 14th International Symposium on Biomedical Imaging (ISBI) |
2017 |
Dermatology Imaging |
Tree-loss function for training neural networks on weakly-labelled datasets |
S. Demyanov, R. Chakravorty, Z. Ge, et al. |
IEEE 14th International Symposium on Biomedical Imaging (ISBI) |
2017 |
Dermatology Imaging |
Investigating Deep Side Layers For Skin Lesion Segmentation |
B. Bozorgtabar, Z. Ge, R. Chakravorty, et al. |
IEEE 14th International Symposium on Biomedical Imaging (ISBI) |
2017 |
Dermatology Imaging |
Skin lesion segmentation using deep convolution networks guided by local unsupervised learning |
B. Bozorgtabar, S. Sedai, P. Roy, R. Garnavi |
IBM R&D Journal |
2017 |
Dermatology Imaging |
Classification of dermoscopy patterns using deep convolutional neural networks |
S. Demyanov, R. Chakravorty, M. Abedini, R. Garnavi |
IEEE 13th International Symposium on Biomedical Imaging (ISBI) |
2016 |
Dermatology Imaging |
Computational Immunohistochemistry: Recipes for Standardization of Immunostaining |
N. M. Arar, P. Pati, A. Kashyap, A. Fomitcheva Khartchenko, G. Kaigala, M. Gabrani |
Medical Image Computing and Computer Assisted Intervention (MICCAI) |
2017 |
Digital Pathology |
Heterogeneity Characterization of Immunohistochemically Stained Tissue using Convolutional Autoencoder |
E. Zerhouni, et. Al |
Proceedings of SPIE |
2017 |
Digital Pathology |
Wide Residual Networks for Mitosis Detection |
E. Zerhouni, et. Al |
IEEE 14th International Symposium on Biomedical Imaging (ISBI) |
2017 |
Digital Pathology |
Disease Grading of Heterogeneous Tissue using Convolutional Autoencoder |
E. Zerhouni, et. Al |
IEEE 14th International Symposium on Biomedical Imaging (ISBI) |
2017 |
Digital Pathology |
Computational Processing of Histological Images |
E. Zerhouni et al. |
ERCIM News, Vol. 108, pp. 14-15 |
2017 |
Digital Pathology |
Image-based computational quantification and visualization of genetic alterations and tumour heterogeneity |
Zhong, Q. et. al. |
Scientic Reports 6(24146) |
2016 |
Digital Pathology |
Big Data takes on Prostate cancer |
Zerhouni, E., Prisacari, B., Zhong, Q., Wild, P., Gabrani, M. |
ERCIM News, 104, pp. 34-35 |
2016 |
Digital Pathology |
Deciphering protein signatures using color, morphological, and topological analysis of immunohistochemically stained human tissues |
Erwan Zerhouni, Bogdan Prisacari, Qing Zhong, Peter Wild, Maria Gabrani |
Proceedings of SPIE Vol. 9791, 97910T |
2016 |
Digital Pathology |
A computational framework for disease grading using protein signatures |
Erwan Zerhouni, Bogdan Prisacari, Qing Zhong, Peter Wild, Maria Gabrani |
13th IEEE International Symposium on Biomedical Imaging, (ISBI 2016), Prague, Czech Republic, April 13-16, 1401-1404 |
2016 |
Digital Pathology |
Hierarchical Deep Learning Ensemble to Automate the Classification of Breast Cancer Pathology Reports by ICD-O Topography |
W. Saib, et. Al |
KDD Workshops |
2018 |
Textual Data Analysis |
TaGiTeD: Predictive Task Guided Tensor Decomposition for Representation Learning from Electronic Health Records |
Yang,K., Li,X., Liu,H., Mei,J., Xie,G., Zhao,J., Xie,B., Wang,F. |
AAAI |
2017 |
Structured Patients Data |
Deep Diabetologist: Learning to Prescribe Hypoglycemia Medications with Hierarchical Recurrent Neural Networks |
J Mei, S Zhao, F Jin, L Zhang, H Liu, X Li |
MedInfo |
2017 |
Structured Patients Data |
Risk prediction with electronic health records: A deep learning approach |
Y.Cheng, F.Wang, P.Zhang, and J. Hu. |
In Proceedings of the SIAM International Conference on Data Mining (pp. 432-440) |
2016 |
Structured Patients Data |
Exploiting Convolutional Neural Network for Risk Prediction with Medical Feature Embedding. |
Z. Che, Y. Cheng, Z. Sun, and Y. Liu. |
NIPS workshop on ML for Health |
2016 |
Structured Patients Data |
Deep State Space Models for Computational Phenotyping. |
S. Ghosh, Y. Cheng, and Z. Sun. |
ICHI |
2016 |
Structured Patients Data |
Measuring Patient Similarities via A Deep Architecture with Medical Concept Embedding |
Z.Zhe, C.Yin, B.Qian, Y.Cheng, J.Wei, F.Wang |
ICDM |
2016 |
Structured Patients Data |
A Deep Learning based Method for Similar Patient Question Retrieval in Chinese |
G Tang, Y Ni, G Xie, X Fan, Y Shi |
MedInfo |
2017 |
Social Textual Data |
NER for Medical Entities in Twitter using Sequence to Sequence Neural Networks |
Jimeno Yepes, A., and MacKinlay, A. |
Australasian Language Technology Association Workshop |
2016 |
Social Textual Data |
Deep Belief Networks and Biomedical Text Categorisation |
Jimeno Yepes, A., MacKinlay, A., Bedo, J., Garnavi, R., and Chen, Q. |
Australasian Language Technology Association Workshop |
2014 |
Social Textual Data |
An efficient multi-scale data representation method for lung nodule false positive reduction using convolutional neural networks |
Dario A.B.Oliveira, Matheus P. Viana |
IEEE 15th International Symposium on Biomedical Imaging (ISBI) |
2018 |
Lung Imaging |
Lung Nodule Malignancy Prediction Using Multi-task Convolutional Neural Network |
Xiuli Li, Yueying Kao, Wei Shen, Xiang Li, Guotong Xie |
SPIE Medical Imaging |
2017 |
Lung Imaging |
Lung nodule detection using 3D convolutional neural networks trained on weakly labeled data |
R.Anirudh, et al. |
SPIE |
2016 |
Lung Imaging |
Exploring Transfer Learning for Gastrointestinal Bleeding Detection on Small-size Imbalanced Endoscopy Images |
Xiuli Li, Hao Zhang, Xiaolu Zhang, Hao Liu and Guotong Xie |
IEEE Engineering in Medicine and Biology Society (EMBC) |
2017 |
Gastrointestinal Imaging |