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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)
Phd Thesis/Master Thesis:
1. [PhD Thesis] Arnes J. I. Toward a Collaborative Platform for Hybrid Designs Sharing a Common Cohort.
2. [Master Thesis] Jadhav S. Reconstructing 3D Geometries of Sub-Cellular Structures from SMLM point clouds. UiT The Arctic University of Norway. Grade: A
3. [Master Thesis] Celeste A. V. Presenting CODS (Cell Organelle Dynamic Simulation). UiT The Arctic University of Norway. Grade: A.
link
4. [Master Thesis] Paul R. Empowering Sami Language Processing A Foundational Model Approach for Low-Resource Languages. UiT The Arctic University of Norway. Grade: A.
Research Schools courses and Workshop organized
1. Summer School Course Tutorial: Ayush Somani. Interpretable DL Playground [GitHub]
2. Arctic LLM Workshop Hands-on Tutorial Session [Link]
3. Course Lecture: INF-8605/INF-3605-1 23V Interpretability in Deep Learning. Course recording released at UiT Panopto [Access] Lecturer: Ayush Somani, Alexander Horsch, Deepak Gupta and Dilip K. Prasad
Research Articles
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]
2. Somani A, Gupta A., Sekh, A.A., Agarwal, K. & Prasad DK. (2024). Blend & Predict: Domain-Adaptable Few-Shot Learning for Microscopy Imaging. In 2024 IEEE International Conference on Image Processing (ICIP). IEEE. [Under Publication]
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]
4. [Book] Somani A, Horsch A, Prasad DK. Interpretability in Deep Learning. (pp. 1-466). Springer. 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.
5. 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. [Link]
6. 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. [Link]
7. 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.
8. Somani A., Horsch A., Bopardikar A., Prasad D. K. Propagating Transparency: A Deep Dive into the Interpretability of Neural Networks. Nordic Machine Intelligence (2023) Special Issue [in press].
9. Biswas, M., Buckchash, H., & Prasad, D. K. (2023). pNNCLR: Stochastic Pseudo Neighborhoods for Contrastive Learning based Unsupervised Representation Learning Problems. Neurocomputing Journal. [Link]
10. Singh, A., Bhambhu, Y., Buckchash, H., Gupta, D. K., & Prasad, D. K. (2023). Latent Graph Attention for Enhanced Spatial Context. Machine Learning. [Link]
11. Agarwal, R., Gupta, D., Horsch, A. and Prasad, D. K. (2023). Aux-Drop: Handling Haphazard Inputs in Online Learning Using Auxiliary Dropouts. Transactions on Machine Learning Research. [Link] [GitHub]
12. 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).
13. 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.
14. 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.
15. 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.
16. 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. [Link]
17. 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.
18. 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).
19. Somani, A., Banerjee, P., Rastogi, M., Habib, A., Agarwal, K. and Prasad, D.K. Image Inpainting with Hypergraphs for Resolution Improvement in Scanning Acoustic Microscopy. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 (pp. 3112-3121). [Link] [Paper Code]
20. 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.
21. 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).
22. 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.
23. 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] [GitHub] [YouTube]
24. 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
25. Gupta, D., Mago, G., Chavan, A., Prasad, D. and Thomas, R.M. (2023). Patch Gradient Descent: Training Neural Networks on Very Large Images. In Workshop on Advancing Neural Network Training: Computational Efficiency, Scalability, and Resource Optimization (WANT@ NeurIPS 2023).
26. Aggarwal, S., Gupta, T., Sahu, P.K., Chavan, A., Tiwari, R., Prasad, D.K. and Gupta, D. K. (2023). On Designing Light-Weight Object Trackers Through Network Pruning: Use CNNs Or Transformers? In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE.
27. 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
28. 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).
29. 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.
3. 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
[GitHub]
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. Somani, A., Sekh, A.A., Opstad, I.S., Birgisdottir, Å.B., Myrmel, T., Ahluwalia, B.S., Horsch, A., Agarwal, K. and Prasad, D.K. (2022). Virtual labeling of mitochondria in living cells using correlative imaging and physics-guided deep learning. Biomedical Optics Express, 13(10), 5495-5516. [Link] [GitHub] [Data Release]
37. 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)
38. 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)
39. Banerjee, P., Akarte, S.M., Kumar, P., Shamsuzzaman, M., Butola, A., Agarwal, K., Prasad, D.K., Melandsø, F. and Habib, A. (2024). High-resolution imaging in acoustic microscopy using deep learning. Machine Learning: Science and Technology, 5(1), 015007. [Link]
40. Singh, D., Somani, A., Horsch, A. and Prasad, D.K. (2022). Counterfactual explainable gastrointestinal and colonoscopy image segmentation. In 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) (pp. 1-5). IEEE. (Link)
41. Weitz, M., Syed, S., Hopstock, L.A., Morseth, B., Prasad, D.K. and Horsch, A. (2022). Discrimination of sleep and wake periods from a hip-worn raw acceleration sensor using recurrent neural networks. medRxiv, 2022-03. (Link)
42. Sumit Rai, Arti Kumari, Dilip K. Prasad (2022). Client Selection in Federated Learning under Imperfections in Environment, AI . (link)
43. 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)
44. 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)
45. 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)
46. Singh, D., Somani, A., Prasad, D. and Horsch, A. (2021). T-MIS: Transparency adaptation in medical image segmentation. Nordic Machine Intelligence, 1(1), 11-13. (Link)
47. 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)
48. Somani, A., Sekh, A.A., Opstad, I.S., Birgisdottir, Å.B., Myrmel, T., Ahluwalia, B.S., Agarwal, K., Prasad, D.K. and Horsch, A. (2021). Digital staining of mitochondria in label-free live-cell microscopy. In Bildverarbeitung für die Medizin 2021: Proceedings, German Workshop on Medical Image Computing, Regensburg, March 7-9, 2021 (pp. 235-240). Springer Fachmedien Wiesbaden. (link)
49. 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)
50. 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.
51. 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)
52. 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)
53. 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.
54. 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.
59. 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.
60. 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)
61. 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
63. Butola, A., Prasad, D.K., Ahmad, A., Dubey, V., Qaiser, D., Srivastava, A., Senthilkumaran, P., Ahluwalia, B.S. and Mehta, D.S. (2020). Deep learning architecture “LightOCT” for diagnostic decision support using optical coherence tomography images of biological samples. Biomedical Optics Express, 11(9), pp.5017-5031. [Link]
57. Arif Ahmed Sekh, Ida S Opstad, Rohit Agarwal, Asa Birna Birgisdottir, Truls Myrmel, Balpreet Singh Ahluwalia, Krishna Agarwal, Dilip K Prasad (2020). Simulation-supervised deep learning for analysing organelles states and behaviour in living cells. arXiv preprint arXiv:2008.12617 (Link)
58. Agarwal, R., Agarwal, K., Horsch, A. and Prasad, D.K. (2022). Auxiliary Network: Scalable and agile online learning for dynamic system with inconsistently available inputs. In International Conference on Neural Information Processing (pp. 549-561). Cham: Springer International Publishing. (Link) [YouTube]
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