Publications of Anindo Saha

all all years 2024 2023 2022 2021 2020

Papers in international journals

  1. N. van Nistelrooij, K. Ghoul, T. Xi, A. Saha, S. Kempers, M. Cenci, B. Loomans, T. Flügge, B. van Ginneken and S. Vinayahalingam, "Combining public datasets for automated tooth assessment in panoramic radiographs", BMC Oral Health, 2024;24.
    Abstract DOI PMID
  2. J. Bosma, A. Saha, M. Hosseinzadeh, I. Slootweg, M. de Rooij and H. Huisman, "Semi-supervised Learning with Report-guided Pseudo Labels for Deep Learning-based Prostate Cancer Detection Using Biparametric MRI", Radiology: Artificial Intelligence, 2023:e230031.
    Abstract DOI Cited by ~5
  3. C. Roest, T. Kwee, A. Saha, J. Futterer, D. Yakar and H. Huisman, "AI-Assisted Biparametric MRI Surveillance of Prostate Cancer: Feasibility Study", European Radiology, 2022.
    Abstract DOI Cited by ~7
  4. M. Sunoqrot, A. Saha, M. Hosseinzadeh, M. Elschot and H. Huisman, "Artificial Intelligence for Prostate MRI: Open Datasets, Available Applications, and Grand Challenges", European Radiology Experimental, 2022:35.
    Abstract DOI Cited by ~26
  5. A. Saha, M. Hosseinzadeh and H. Huisman, "End-to-end Prostate Cancer Detection in bpMRI via 3D CNNs: Effects of Attention Mechanisms, Clinical Priori and Decoupled False Positive Reduction", Medical Image Analysis, 2021:102155.
    Abstract DOI Algorithm Download Cited by ~87
  6. M. Hosseinzadeh, A. Saha, P. Brand, I. Slootweg, M. de Rooij and H. Huisman, "Deep learning-assisted prostate cancer detection on bi-parametric MRI: minimum training data size requirements and effect of prior knowledge", European Radiology, 2021.
    Abstract DOI Download Cited by ~46

Preprints

  1. M. Hosseinzadeh, A. Saha, J. Bosma and H. Huisman, "Uncertainty-Aware Semi-Supervised Learning for Prostate MRI Zonal Segmentation", arXiv:2305.05984, 2023.
    Abstract DOI arXiv Cited by ~1
  2. B. de Wilde, A. Saha, R. ten Broek and H. Huisman, "Medical diffusion on a budget: textual inversion for medical image generation", arXiv:2303.13430, 2023.
    Abstract arXiv Cited by ~5
  3. M. Eisenmann, A. Reinke, V. Weru, M. Tizabi, F. Isensee, T. Adler, P. Godau, V. Cheplygina, M. Kozubek, S. Ali, A. Gupta, J. Kybic, A. Noble, C. de Solórzano, S. Pachade, C. Petitjean, D. Sage, D. Wei, E. Wilden, D. Alapatt, V. Andrearczyk, U. Baid, S. Bakas, N. Balu, S. Bano, V. Bawa, J. Bernal, S. Bodenstedt, A. Casella, J. Choi, O. Commowick, M. Daum, A. Depeursinge, R. Dorent, J. Egger, H. Eichhorn, S. Engelhardt, M. Ganz, G. Girard, L. Hansen, M. Heinrich, N. Heller, A. Hering, A. Huaulmé, H. Kim, B. Landman, H. Li, J. Li, J. Ma, A. Martel, C. Martín-Isla, B. Menze, C. Nwoye, V. Oreiller, N. Padoy, S. Pati, K. Payette, C. Sudre, K. van Wijnen, A. Vardazaryan, T. Vercauteren, M. Wagner, C. Wang, M. Yap, Z. Yu, C. Yuan, M. Zenk, A. Zia, D. Zimmerer, R. Bao, C. Choi, A. Cohen, O. Dzyubachyk, A. Galdran, T. Gan, T. Guo, P. Gupta, M. Haithami, E. Ho, I. Jang, Z. Li, Z. Luo, F. Lux, S. Makrogiannis, D. Müller, Y. Oh, S. Pang, C. Pape, G. Polat, C. Reed, K. Ryu, T. Scherr, V. Thambawita, H. Wang, X. Wang, K. Xu, H. Yeh, D. Yeo, Y. Yuan, Y. Zeng, X. Zhao, J. Abbing, J. Adam, N. Adluru, N. Agethen, S. Ahmed, Y. Khalil, M. Alenyà, E. Alhoniemi, C. An, T. Anwar, T. Arega, N. Avisdris, D. Aydogan, Y. Bai, M. Calisto, B. Basaran, M. Beetz, C. Bian, H. Bian, K. Blansit, L. Bloch, R. Bohnsack, S. Bosticardo, J. Breen, M. Brudfors, R. Brüngel, M. Cabezas, A. Cacciola, Z. Chen, Y. Chen, D. Chen, M. Cho, M. Choi, C. Xie, D. Cobzas, J. Cohen-Adad, J. Acero, S. Das, M. de Oliveira, H. Deng, G. Dong, L. Doorenbos, C. Efird, S. Escalera, D. Fan, M. Serj, A. Fenneteau, L. Fidon, P. Filipiak, R. Finzel, N. Freitas, C. Friedrich, M. Fulton, F. Gaida, F. Galati, C. Galazis, C. Gan, Z. Gao, S. Gao, M. Gazda, B. Gerats, N. Getty, A. Gibicar, R. Gifford, S. Gohil, M. Grammatikopoulou, D. Grzech, O. Güley, T. Günnemann, C. Guo, S. Guy, H. Ha, L. Han, I. Han, A. Hatamizadeh, T. He, J. Heo, S. Hitziger, S. Hong, S. Hong, R. Huang, Z. Huang, M. Huellebrand, S. Huschauer, M. Hussain, T. Inubushi, E. Polat, M. Jafaritadi, S. Jeong, B. Jian, Y. Jiang, Z. Jiang, Y. Jin, S. Joshi, A. Kadkhodamohammadi, R. Kamraoui, I. Kang, J. Kang, D. Karimi, A. Khademi, M. Khan, S. Khan, R. Khantwal, K. Kim, T. Kline, S. Kondo, E. Kontio, A. Krenzer, A. Kroviakov, H. Kuijf, S. Kumar, F. La Rosa, A. Lad, D. Lee, M. Lee, C. Lena, H. Li, L. Li, X. Li, F. Liao, K. Liao, A. Oliveira, C. Lin, S. Lin, A. Linardos, M. Linguraru, H. Liu, T. Liu, D. Liu, Y. Liu, J. Lourenço-Silva, J. Lu, J. Lu, I. Luengo, C. Lund, H. Luu, Y. Lv, Y. Lv, U. Macar, L. Maechler, S. L., K. Marshall, M. Mazher, R. McKinley, A. Medela, F. Meissen, M. Meng, D. Miller, S. Mirjahanmardi, A. Mishra, S. Mitha, H. Mohy-ud-Din, T. Mok, G. Murugesan, E. Karthik, S. Nalawade, J. Nalepa, M. Naser, R. Nateghi, H. Naveed, Q. Nguyen, C. Quoc, B. Nichyporuk, B. Oliveira, D. Owen, J. Pal, J. Pan, W. Pan, W. Pang, B. Park, V. Pawar, K. Pawar, M. Peven, L. Philipp, T. Pieciak, S. Plotka, M. Plutat, F. Pourakpour, D. Preloznik, K. Punithakumar, A. Qayyum, S. Queirós, A. Rahmim, S. Razavi, J. Ren, M. Rezaei, J. Rico, Z. Rieu, M. Rink, J. Roth, Y. Ruiz-Gonzalez, N. Saeed, A. Saha, M. Salem, R. Sanchez-Matilla, K. Schilling, W. Shao, Z. Shen, R. Shi, P. Shi, D. Sobotka, T. Soulier, B. Fadida, D. Stoyanov, T. Mun, X. Sun, R. Tao, F. Thaler, A. Théberge, F. Thielke, H. Torres, K. Wahid, J. Wang, Y. Wang, W. Wang, X. Wang, J. Wen, N. Wen, M. Wodzinski, Y. Wu, F. Xia, T. Xiang, C. Xiaofei, L. Xu, T. Xue, Y. Yang, L. Yang, K. Yao, H. Yao, A. Yazdani, M. Yip, H. Yoo, F. Yousefirizi, S. Yu, L. Yu, J. Zamora, R. Zeineldin, D. Zeng, J. Zhang, B. Zhang, J. Zhang, F. Zhang, H. Zhang, Z. Zhao, Z. Zhao, J. Zhao, C. Zhao, Q. Zheng, Y. Zhi, Z. Zhou, B. Zou, K. Maier-Hein, P. Jäger, A. Kopp-Schneider and L. Maier-Hein, "Biomedical image analysis competitions: The state of current participation practice", arXiv:2212.08568, 2022.
    Abstract DOI arXiv Cited by ~17
  4. J. Bosma, A. Saha, M. Hosseinzadeh, I. Slootweg, M. de Rooij and H. Huisman, "Annotation-efficient cancer detection with report-guided lesion annotation for deep learning-based prostate cancer detection in bpMRI", arXiv:2112.05151, 2021.
    Abstract DOI arXiv Cited by ~8

Papers in conference proceedings

  1. A. Saha, J. Bosma, J. Twilt, B. van Ginneken, D. Yakar, M. Elschot, J. Veltman, J. Futterer, M. de Rooij and H. Huisman, "Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI: The PI-CAI Challenge", Medical Imaging with Deep Learning, 2023.
    Abstract Url
  2. J. Bosma, D. Peeters, N. Alves, A. Saha, Z. Saghir, C. Jacobs and H. Huisman, "Reproducibility of Training Deep Learning Models for Medical Image Analysis", Medical Imaging with Deep Learning, 2023.
    Abstract Url
  3. A. Saha, J. Bosma, J. Linmans, M. Hosseinzadeh and H. Huisman, "Anatomical and Diagnostic Bayesian Segmentation in Prostate MRI -- Should Different Clinical Objectives Mandate Different Loss Functions?", Medical Imaging Meets NeurIPS Workshop - 35th Conference on Neural Information Processing Systems (NeurIPS), 2021.
    Abstract arXiv Cited by ~6
  4. A. Saha, M. Hosseinzadeh and H. Huisman, "Encoding Clinical Priori in 3D Convolutional Neural Networks for Prostate Cancer Detection in bpMRI", Medical Imaging Meets NeurIPS Workshop - 34th Conference on Neural Information Processing Systems (NeurIPS), 2020.
    Abstract arXiv Cited by ~6
  5. A. Saha, F. Tushar, K. Faryna, V. D'Anniballe, R. Hou, M. Mazurowski, G. Rubin and J. Lo, "Weakly Supervised 3D Classification of Chest CT using Aggregated Multi-Resolution Deep Segmentation Features", Medical Imaging, 2020;11314:39 - 44.
    Abstract DOI arXiv
  6. A. Saha, P. Prasad and A. Thabit, "Leveraging Adaptive Color Augmentation in Convolutional Neural Networks for Deep Skin Lesion Segmentation", IEEE International Symposium on Biomedical Imaging, 2020:2014-2017.
    Abstract DOI arXiv

Abstracts

  1. A. Saha, J. Bosma, J. Twilt, B. van Ginneken, D. Yakar, M. Elschot, J. Veltman, J. Futterer, M. de Rooij and H. Huisman, "Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI: The PI-CAI Challenge", European Congress of Radiology, 2023.
    Abstract
  2. J. Twilt, A. Saha, J. Bosma, B. van Ginneken, D. Yakar, M. Elschot, J. Veltman, J. Futterer, H. Huisman and M. de Rooij, "EAU Plenary Gamechanging Session - Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI: Preliminary Results from the PI-CAI Challenge", Annual European Association of Urology Congress, 2023.
    Abstract
  3. J. Twilt, A. Saha, J. Bosma, B. van Ginneken, D. Yakar, M. Elschot, J. Veltman, J. Futterer, H. Huisman and M. de Rooij, "International Comparative Study of Artificial Intelligence and Radiologists in Clinically Significant Prostate Cancer Detection: Results From The PI-CAI Consortium", Annual Meeting of the Society for Advanced Body Imaging, 2023.
    Abstract
  4. J. Twilt, A. Saha, J. Bosma, B. van Ginneken, D. Yakar, M. Elschot, J. Veltman, J. Futterer, H. Huisman and M. de Rooij, "Diagnostic Value of Dynamic Contrast-Enhanced MRI for the Detection of Clinically Significant Prostate Cancer in a Multi-Reader Study: Preliminary Results from the PI-CAI Consortium", European Congress of Radiology, 2023.
    Abstract
  5. C. Roest, T. Kwee, A. Saha, J. Futterer, D. Yakar and H. Huisman, "AI-Assisted Biparametric MRI Surveillance of Prostate Cancer: Feasibility Study", European Congress of Radiology, 2022.
    Abstract
  6. A. Saha, J. Bosma, J. Twilt, B. van Ginneken, D. Yakar, M. Elschot, J. Veltman, J. Futterer, M. de Rooij and H. Huisman, "Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI: The PI-CAI Challenge", Annual Meeting of the Radiological Society of North America, 2022.
    Abstract
  7. A. Saha, J. Bosma, C. Roest, M. Hosseinzadeh, J. Futterer and H. Huisman, "Deep Learning with Bayesian Inference for Prostate Cancer Diagnosis across Longitudinal Biparametric MRI", Annual Meeting of the Radiological Society of North America, 2021.
    Abstract
  8. J. Bosma, A. Saha, M. Hosseinzadeh and H. Huisman, "Augmenting AI with Automated Segmentation of Report Findings Applied to Prostate Cancer Detection in Biparametric MRI", Annual Meeting of the Radiological Society of North America, 2021.
    Abstract

Master theses

  1. S. Adilina, A. Saha and H. Huisman, "Domain Generalization for Prostate Cancer Detection in MRI", Master thesis, 2022.
    Abstract Url
  2. J. Bosma, A. Saha, M. Hosseinzadeh and H. Huisman, "Augmenting AI with Automated Segmentation of Report Findings Applied to Prostate Cancer Detection in Biparametric MRI", Master thesis, 2021.
    Abstract Url
  3. A. Saha, M. Hosseinzadeh and H. Huisman, "Computer-Aided Detection of Clinically Significant Prostate Cancer in mpMRI", Master thesis, 2020.
    Abstract Url