top of page
Publications
The researchers in our team regularly have their works published in top scientific journals. You'll find our recent publications mentioned here.
Abstract: Mitochondria play a crucial role in cellular metabolism. This paper presents a novel method to visualize mitochondria in living cells without the use of fluorescent markers. We propose a physics-guided deep learning approach for obtaining virtually labeled micrographs of mitochondria from bright-field images. We integrate a microscope’s point spread function in the learning of an adversarial neural network for improving virtual labeling. We show results (average Pearson correlation 0.86) significantly better than what was achieved by state-of-the-art (0.71) for virtual labeling of mitochondria. We also provide new insights into the virtual labeling problem and suggest additional metrics for quality assessment. The results show that our virtual labeling approach is a powerful way of segmenting and tracking individual mitochondria in bright-field images, results previously achievable only for fluorescently labeled mitochondria.
Biomedical Optics Express: Vol. 13, Issue 10, pp. 5495-5516 (2022)
1. Naskar, G., Mohiuddin, S., Malakar, S., Cuevas, E., & Sarkar, R. (2024). Deepfake Detection using Deep Feature Stacking and Meta-learning. Heliyon 10 (4), e25933.Link to the publication: https://www.sciencedirect.com/science/article/pii/S2405844024019649
2. Banerjee, N., Malakar, S., Gupta, D. K., Horsch, A., & Prasad, D. K. (2023, November). Guided U-Net Aided Efficient Image Data Storing with Shape Preservation. In Asian Conference on Pattern Recognition (pp. 317-330). Cham: Springer Nature. Switzerland. Link to the publication: https://link.springer.com/chapter/10.1007/978-3-031-47634-1_24
3. Ghosal, K., Singh, A., Malakar, S., & Gupta, D. (2023). Deep Learning based Joint Inversion of Electrical Resistivity Tomography and Radio Magnetotelluric Data. AGU23. Link to the publication: https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1313132
4 [Book] Somani A, Horsch A, Prasad DK. Interpretability in Deep Learning. Springer Cham; 1st ed. 2023 edition(Hardcover ISBN : 978-3-031-20638-2; Published: 01 May 2023) https://doi.org/10.1007/978-3-031-20639-9 . . UiT The Arctic University of Norway.
5. [PhD Thesis] J. I. Arnes, Toward a Collaborative Platform for Hybrid Designs Sharing a Common Cohort.
6. [Master Thesis] S. Jadhav, Reconstructing 3D Geometries of Sub-Cellular Structures from SMLM point clouds. UiT The Arctic University of Norway. Grade: A
7. [Master Thesis] A. V. Celeste, Presenting CODS (Cell Organelle Dynamic Simulation). UiT The Arctic University of Norway. Grade: A. link
8. Punnakkal, A. R., Godtliebsen, G., Somani, A., Maldonado, S. A. A., Birgisdottir, Å. B., Prasad, D. K., ... & Agarwal, K. (2023). Analyzing Mitochondrial Morphology Through Simulation Supervised Learning. JoVE (Journal of Visualized Experiments), (193), e64880.
9. Godtliebsen, G., Larsen, K. B., Bhujabal, Z., Opstad, I. S., Nager, M., Punnakkal, A. R., ... & Birgisdottir, A. B. (2023). High-resolution visualization and assessment of basal and OXPHOS-induced mitophagy in H9c2 cardiomyoblasts. Autophagy, 19(10), 2769-2788.
10. Malakar, S., Sen, S., Romanov, S., Kaplun, D., & Sarkar, R. (2023). Role of transfer functions in PSO to select diagnostic attributes for chronic disease prediction: An experimental study. Journal of King Saud University-Computer and Information Sciences, 35(9), 101757.
11. Somani A, Horsch A, Bopardikar Ajit, Prasad DK. Propagating Transparency: A Deep Dive into the Interpretability of Neural Networks. Nordic Machine Intelligence (2023) Special Issue [in press].
12. Biswas, M., Buckchash, H., & Prasad, D. K. (2023). pNNCLR: Stochastic Pseudo Neighborhoods for Contrastive Learning based Unsupervised Representation Learning Problems. Neurocomputing Journal. doi.org/10.48550/arXiv.2308.06983
13. Singh, A., Bhambhu, Y., Buckchash, H., Gupta, D. K., & Prasad, D. K. (2023). Latent Graph Attention for Enhanced Spatial Context. Machine Learning. doi.org/10.48550/arXiv.2307.04149
14. Agarwal, Rohit, et al. "Aux-Drop: Handling Haphazard Inputs in Online Learning Using Auxiliary Dropouts." Transactions on Machine Learning Research (2023). link
15. Jadhav, S., Kuchibhotla, R., Agarwal, K., Habib, A., & Prasad, D. K. (2023). Deep learning-based denoising of acoustic images generated with point contact method. Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems, 6(3).
16. Bhatt, S., Butola, A., Kumar, A., Thapa, P., Joshi, A., Jadhav, S., ... & Mehta, D. S. (2023). Single-shot multispectral quantitative phase imaging of biological samples using deep learning. Applied Optics,
62(15), 3989-3999.
17. Arora, G., Butola, A., Rajput, R., Agarwal, R., Agarwal, K., Horsch, A., ... & Senthilkumaran, P. (2023). Taxonomy of hybridly polarized Stokes vortex beams. Optics Express. Accepted.
18. Dong, H., Zhou, J., Qiu, C., Prasad, D. K., & Chen, I. M. (2022). Robotic manipulations of cylinders and ellipsoids by ellipse detection with domain randomization. IEEE/ASME Transactions on Mechatronics,
28(1), 302-313.
19. Johannessen, E., Johansson, J., Hartvigsen, G., Horsch, A., Årsand, E., &Henriksen, A. (2023). Collecting health-related research data using consumer-based wireless smart scales. International Journal of Medical
Informatics, 173, 105043.
20. Johannessen, E., Henriksen, A., Årsand, E., Horsch, A., Johansson, J., & Hartvigsen, G. (2023). Health research requires efficient platforms for data collection from personal devices,Studies in Health Technology and
Informatics; 302.
21. Barken, T. L., Bonacina, S., Bostad, R., Gabarron, E., Garcia, B., Haddeland, K., ... & Årsand, E. (2023). University campus as a smart technology-supported active learning arena. Septentrio Reports, (1).
22. Banerjee, P., Mishra, S., Yadav, N., Agarwal, K., Melandsø, F., Prasad, D. K., & Habib, A. (2023). Image inpainting in acoustic microscopy. AIP Advances, 13(4).
23. Dong, H., Zhou, J., Qiu, C., Prasad, D. K., & Chen, I. M. (2022). Robotic manipulations of cylinders and ellipsoids by ellipse detection with domain randomization. IEEE/ASME Transactions on Mechatronics,
28(1), 302-313.
24. Somani A, Banerjee P, Rastogi M, Agarwal K, Prasad DK, Habib A. Image Inpainting with Hypergraphs for Resolution Improvement in Scanning Acoustic Microscopy. In Proceedings of the IEEE/CVF Conference onComputer Vision and Pattern Recognition 2023 (pp. 3112-3121).
25. Banerjee, N., Malakar, S., Gupta, D. K., Horsch, A., & Prasad, D. K. (2023, November). Guided U-Net Aided Efficient Image Data Storing with Shape Preservation. In Asian Conference on Pattern Recognition. Cham: Springer Nature Switzerland.
26. Agarwal, Rohit, et al. "Mabnet: Master Assistant Buddy Network with Hybrid Learning for Image Retrieval." ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023. Link
27. S. Jadhav, S. Majhi, A.S. Chowdhury, D.K.. Prasad, K. Agarwal, Reconstructing 3D shape from 3D ThunderSTORM Point Clouds Focus on Microscopy Conference 2023, Porto, Portugal
28. Gupta, Deepak K; Mago, Gowreesh; Chavan, Arnav; Prasad, Dilip K; ,Patch Gradient Descent: Training Neural Networks on Very Large Images, NeurIPS Workshop,,2023.
29. Aggarwal, Saksham; Gupta, Taneesh; Sahu, Pawan; Chavan, Arnav; Tiwari, Risabh; Prasad, Dilip K.; Gupta, Deepak;. On Designing Light-Weight Object Trackers Through Network Pruning: Use CNNs Or Transformers?, IEEE International Conference on Acoustics, Speech and Signal Processing",2023.
30. Tiwari, Rishabh; Chavan, Arnav; Gupta, Deepak; Mago, Gowreesh; Gupta, Animesh; Gupta, Akash; Sharan, Suraj; Yang, Yukun; Zhao, Shanwei; Wang, Shihao. RCV2023 Challenges: Benchmarking Model Training and Inference for Resource-Constrained Deep Learning, IEEE/CVF International Conference on Computer Vision, pp. 1534-1543, 2023
31. Bamba, U., Anand, N., Aggarwal, S., Prasad, D. K., & Gupta, D. K. (Accepted 2023). Partial Binarization of Neural Networks for Budget-Aware Efficient Learning. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 2336-2345).
32. Punnakkal, A. R., Jadhav, S. S., Horsch, A., Agarwal, K., & Prasad, D. K. (2023). MiShape: 3D Shape Modelling of Mitochondria in Microscopy. arXiv preprint arXiv:2303.01546.
33. Iqra Qasim, Alexander Horsch, and Dilip K. Prasad. 2023. Dense Video Captioning: A survey of Techniques, Datasets, and Evaluation Protocols. Submitted to ACM Computing Surveys https://doi.org/10.48550/arXiv.2311.02538
34. Agarwal, Rohit, et al. "Modelling Irregularly Sampled Time Series Without Imputation." arXiv preprint arXiv:2309.08698 (2023). link
35. S. Jadhav, S. Majhi, A.S. Chowdhury, D.K.. Prasad, K. Agarwal, Reconstructing 3D shape from 3D ThunderSTORM Point Clouds Focus on Microscopy Conference 2023, Porto, Portugal Datesets/codesets release
36. Tool & Video Protocol: Tool on fluorescence microscopy images of fixed cardiomyoblasts.
37. Paper Code: Image Inpainting With Hypergraphs for Resolution Improvement in Scanning Acoustic Microscopy[CVPR Workshop 2023]
38. Summer School Course Tutorial: Interpretable DL Playground
39. Paper Code: Virtual Labeling of Mitochondria in Living Cells using Physics-guided Deep Learning [Data Release]
40. Lab’s Repository Management: Arctic LLM Workshop Hands-on Tutorial Session
41. Aux-Drop: https://github.com/Rohit102497/Aux-Drop
42. Mabnet: https://github.com/Rohit102497/MABNet
43. Modelling Irregularly Sampled Time Series Without Imputation: https://github.com/Rohit102497/SLAN
44. Video Lecture: INF-8605/3605 [TEKNOBYGGET 1.022AUD] Course recording released at UiT Panopto
45. YouTube video of Auxiliary Network Paper: Link
46. YouTube video of MABNet: Link
44. A Somani, AA Sekh, IS Opstad, AB Birgisdottir, T Myrmel, BS Ahluwalia (2022). Virtual Labeling of Mitochondria in Living Cells using Correlative Imaging and Physics-guided Deep Learning Biomedical Optics Express 10.1364/BOE.464177. (link)
45. Soham Chattopadhyay, Antoni Malachowski, J. K. Swain, R. A. Dalmo, A. Horsch, D. K. Prasad (2022). Mapping functional changes in the embryonic heart of Atlantic salmon post viral infection using AI technique. IEEE International Conference on Image Processing (ICIP).(link)
46. Deepak K Gupta, Udbhav Bamba, Abhishek Thakur, Akash Gupta, Suraj Sharan, Ertugrul Demir, Dilip K Prasad (2022). UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images. arXiv preprint arXiv:2206.12681 (link)
47. Pragyan Banerjee, Shivam Milind Akarte, Prakhar Kumar, Muhammad Shamsuzzaman, Krishna Agarwal, Ankit Butola, Dilip K Prasad, Frank Melandsø, Habib Anowarul (2022). High-resolution imaging in acoustic microscopy using deep learning. (link)
48. D Singh, A Somani, A Horsch, DK Prasad (2022). Counterfactual Explainable Gastrointestinal and Colonoscopy Image Segmentation. International Symposium on Biomedical Imaging (ISBI). (link)
49. Marc Weitz, Shaheen Syed, Laila Arnesdatter Hopstock, Bente Morseth, Dilip Kumar Prasad, Alexander Horsch (2022). Discrimination of sleep and wake periods from a hip-worn raw acceleration sensor using recurrent neural networks. (link)
50. Sumit Rai, Arti Kumari, Dilip K. Prasad (2022). Client Selection in Federated Learning under Imperfections in Environment, AI . (link)
51. Zicheng Liu, Mayank Roy, Dilip K Prasad, Krishna Agarwal (2022). Physics-guided Loss Functions Improve Deep Learning Performance in Inverse Scattering. IEEE Transactions on Computational Imaging. (link)
52. A.A. Sekh, I-S. Opstad, G Godtliebsen, A.B. Birgisdottir, B.S. Ahluwalia, K. Agarwal, D.K. Prasad (2021). Physics based machine learning for sub-cellular segmentation in living cells. Nature Machine Intelligence. (link)
53. S Chattopadhyay, L Zary, C Quek, Dilip K. Prasad (2021). Motivation detection using EEG signal analysis by residual-in-residual convolutional neural network. Expert Systems With Applications. 10.1016/j.eswa.2021.115548 (link)
55. Hui Xue, Bjørn-Morten Batalden, Puneet Sharma, Jarle André Johansen, Dilip K. Prasad. (2021). Applied Sciences. Biosignals based driving skill classification using machine learning: a case study of maritime navigation. 10.3390/app11209765 (link)
56. Somani, Ayush & Sk, Arif Ahmed & Opstad, Ida & Birgisdottir, Asa & Myrmel, Truls & Ahluwalia, Balpreet & Agarwal, Krishna & Prasad, Dilip & Horsch, Alexander. (2021). Digital Staining of Mitochondria in Label-free Live-cell Microscopy. 10.1007/978-3-658-33198-6_55. (link)
57. Joshi, Deepa & Butola, Ankit & Kanade, Sheetal & Prasad, Dilip & Sevanthi, Amitha Mithra & Singh, N & Bisht, Deepak & Mehta, Dalip. (2021). Label-free non-invasive classification of rice seeds using optical coherence tomography assisted with deep neural network. Optics & Laser Technology. 137. 10.1016/j.optlastec.2020.106861. (link)
58. Jadhav, Suyog & Acuña M., Sebastian & Opstad, Ida & Ahluwalia, Balpreet & Agarwal, Krishna & Prasad, Dilip. (2021). Artefact removal in ground truth deficient fluctuations-based nanoscopy images using deep learning. Biomedical Optics Express. 12. 191. 10.1364/BOE.410617.
59. Zhe, Quah & Sk, Arif Ahmed & Quek, Chai & Prasad, Dilip. (2021). Recurrent Self-evolving Takagi–Sugeno–Kan Fuzzy Neural Network (RST-FNN) Based Type-2 Diabetic Modeling. 10.1007/978-3-030-74826-5_11. (link)
60. Jun, Seow & Sk, Arif Ahmed & Quek, Chai & Prasad, Dilip. (2021). seMLP: Self-evolving Multi-layer Perceptron in Stock Trading Decision Making. SN Computer Science. 2. 10.1007/s42979-021-00524-9. (link)
61. Liu, Feng & Sk, Arif Ahmed & Quek, Chai & Ng, Geok & Prasad, Dilip. (2021). RS-HeRR: a rough set-based Hebbian rule reduction neuro-fuzzy system. Neural Computing and Applications. 33. 10.1007/s00521-020-04997-2.
62. Ströhl, Florian & Jadhav, Suyog & Ahluwalia, Balpreet & Agarwal, Krishna & Prasad, Dilip. (2020). Object detection neural network improves Fourier ptychography reconstruction. Optics Express. 28. 37199. 10.1364/OE.409679.
67. A Singh, A Bhave, Dilip K Prasad (2020). Single image dehazing for a variety of haze scenarios using back projected pyramid network. European Conference on Computer Vision Workshops.
68. A. A Sekh, I-S. Opstad, AB Birgisdottir, T Myrmel, BS Ahluwalia, K Agarwal, Dilip K Prasad (2020). Learning nanoscale motion patterns of vesicles in living cells. IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
69. Ratnabali Pal, Arif Ahmed Sekh, Samarjit Kar, Dilip K Prasad (2020). Neural network based country wise risk prediction of COVID-19. Applied Science. /10.3390/app10186448
71. Ankit Butola, Dilip K Prasad, Azeem Ahmad, Vishesh Dubey, Darakhshan Qaiser, Anurag Srivastava, Paramsivam Senthilkumaran, Balpreet Singh Ahluwalia, Dalip Singh Mehta (2020). Deep learning architecture LightOCT for diagnostic decision support using optical coherence tomography images of biological samples. Biomedical Optics Express 10.1364/BOE.395487
bottom of page