Dr. Deepak Ranjan Nayak

Assistant Professor (Grade-I)

Department of Computer Science and Engineering,

Department of Artificial Intelligence and Data Engineering,

Malaviya National Institute of Technology, Jaipur.

Email: drnayak.cse[at]mnit[dot]ac[dot]in, depakranjannayak[at]gmail[dot]com

Google Scholar | Scopus | Web of Sciences

My research focuses on :

Driving innovation at the intersection of medical imaging and intelligent diagnostics, enabling next-gen healthcare solutions.
Building scalable ML pipelines with CNNs, Transformers, and Foundation Models for robust disease detection and progression analysis.

From SAM to DINOv2: Towards Distilling Foundation Models to Lightweight Baselines for Generalized Polyp Segmentation.

Shivanshu Agnihotri, Snehashis Majhi, Deepak Ranjan Nayak, Debesh Jha

Winter Conference on Applications of Computer Vision (WACV 2026)

🌐 CODE | 📄 PDF | ✅ Supplementary Materials

Mamba Guided Boundary Prior Matters: A New Perspective for Generalized Polyp Segmentation.

Tapas K Dutta, Snehashis Majhi, Deepak Ranjan Nayak, Debesh Jha

International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2025) (Oral and Highlight)

🌐 CODE | 📄 PDF | ✅ Supplementary Materials

SAM-Mamba: Mamba Guided SAM Architecture for Generalized Zero-Shot Polyp Segmentation.

Tapas Kumar Dutta, Snehashis Majhi, Deepak Ranjan Nayak, Debesh Jha

Winter Conference on Applications of Computer Vision (WACV 2025)

🌐 CODE | 📄 PDF | ✅ Supplementary Materials

Histoformer: A Frequency Domain-based Convolutional Transformer for Improved Breast Cancer Classification.

Babita, Deepak Ranjan Nayak

IEEE Transactions on Artificial Intelligence (2025)

🌐 CODE | 📄 PDF | ✅ Supplementary Materials

Multi-Dimensional Weighted Attention-Guided Network for Diabetic Retinopathy Grading.

Dipankar Das, Arvind Okram, Deepak Ranjan Nayak, Tapan Kumar Gandhi

IEEE Transactions on Instrumentation and Measurement (2025)

🌐 CODE | 📄 PDF | ✅ Supplementary Materials

Glaucoformer: Dual-domain global transformer network for generalized glaucoma stage classification.

D Das, D. R. Nayak, R. B. Pachori

IEEE Journal of Biomedical and Health Informatics (2025)

🌐 CODE | 📄 PDF | ✅ Supplementary Materials

CaDT-Net: A Cascaded Deformable Transformer Network for Multiclass Breast Cancer Histopathological Image Classification.

Babita, Kadali Sri Akash, M Sajid, Deepak Ranjan Nayak, M Tanveer

International Conference on Neural Information Processing (2024)

🌐 CODE | 📄 PDF | ✅ Supplementary Materials

MCT-Net: a lightweight multiscale convolutional transformer network for polyp segmentation.

Niladri Chakraborti, Deepak Ranjan Nayak

IEEE International Conference on Image Processing (ICIP 2024)

🌐 CODE | 📄 PDF | ✅ Supplementary Materials

FJA-Net: A fuzzy joint attention guided network for classification of glaucoma stages.

Dipankar Das, Deepak Ranjan Nayak

IEEE Transactions on Fuzzy Systems (2024)

🌐 CODE | 📄 PDF | ✅ Supplementary Materials

RDTNet: A residual deformable attention based transformer network for breast cancer classification.

Babita, Deepak Ranjan Nayak

Expert Systems with Applications (2024)

🌐 CODE | 📄 PDF | ✅ Supplementary Materials

Gs-net: Global self-attention guided cnn for multi-stage glaucoma classification.

Dipankar Das, Deepak Ranjan Nayak

IEEE International Conference on Image Processing (ICIP 2023)

🌐 CODE | 📄 PDF | ✅ Supplementary Materials

CDANet: Channel split dual attention based CNN for brain tumor classification in MR images

Tapas Kumar Dutta, Deepak Ranjan Nayak

IEEE International Conference on Image Processing (ICIP 2023)

🌐 CODE | 📄 PDF | ✅ Supplementary Materials

CA-Net: A novel cascaded attention-based network for multi-stage glaucoma classification using fundus images.

Dipankar Das, Deepak Ranjan Nayak, Ram Bilas Pachori

IEEE Transactions on Instrumentation & Measurement (2023)

🌐 CODE | 📄 PDF | ✅ Supplementary Materials

Multi-level 3dcnn with min-max ranking loss for weakly-supervised video anomaly detection.

Snehashis Majhi, Deepak Ranjan Nayak, Ratnakar Dash, Pankaj Kumar Sa

International Conference on Neural Information Processing (2022)

🌐 CODE | 📄 PDF | ✅ Supplementary Materials

MFL-Net: An Efficient Lightweight Multi-Scale Feature Learning CNN for COVID-19 Diagnosis from CT Images.

Amogh Manoj Joshi, Deepak Ranjan Nayak

IEEE Journal of Biomedical and Health Informatics (2022)

🌐 CODE | 📄 PDF | ✅ Supplementary Materials

Open Positions

Limited Winter/Summer Internship opportunities are available. You can contact me via email.
I am looking for highly motivated Ph.D., M.Tech, and B.Tech students to join my research group. Interested candidates may drop me an email with their CV.

Minimum requirements: Excellent knowledge of programming languages (Python, Matlab, etc.)
Basic knowledge of Computer Vision or Machine Learning will be good to have.

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