publications

2026

1. cmu-grf-2026

   Estimating Neurovascular Deformation During Thrombectomy From 2D Fluoroscopy Using Physics-Regularized Deep Graph Networks

   Tushar Nayak, Rishi Basdeo, Edward Andrews, and 2 more authors

   Poster presentation, Carnegie Mellon University Graduate Research Forum, 2026 · poster
2. glioma-review

   The Use of Artificial Intelligence and Machine Learning to Predict Tumor Recurrence in High-Grade Gliomas: A Systematic Review

   Trent Kite, Tushar Nayak, Stephen Jaffee, and 7 more authors

   Neurosurgical Review, 2026 · journal

   DOI
3. elsr-iros-2026

   

   Toward Real-Time 3D Vascular State Estimation from Monocular Fluoroscopy for Endovascular Robotic Surgery

   Tushar Nayak, Rishi Basdeo, Edward Andrews, and 2 more authors

   In 2nd International Workshop on the Evolving Landscape of Surgical Robotics (ELSR), IEEE/RSJ IROS 2026, 2026 · conference

   Poster
4. fluorogrid-manuscript

   Real-Time 3D Neuro-Vascular Deformation Estimation from Sparse 2D Angiography

   Tushar Nayak, Rishi Basdeo, Edward Andrews, and 2 more authors

   2026

   Manuscript in preparation for submission to IPCAI 2027 / International Journal of Computer Assisted Radiology and Surgery (IJCARS) · under review
5. echo-recon-manuscript

   Sparse-View Cardiac Reconstruction with Neural Implicit Representations

   Tushar Nayak, Vivek Dhara, and Vaibhav Parekh

   2026

   Manuscript in preparation · under review

   Abs Code

   Recovering cardiac geometry from sparse annotated sections requires a representation that fits the observed anatomy while constraining unobserved regions. We study coordinate-based implicit neural representations (INRs) for completing the left-ventricular label of the MITEA dataset from three known sections, scoring only voxels outside the observed sections. A compact per-subject Fourier-feature INR fitted to three orthogonal sections reaches unobserved-voxel Dice of 0.932 on a development subject, against 0.899 for a Gaussian occupancy field trained on identical observations. Mesh diagnostics show that this overlap coexists with small disconnected fragments, and that integrated Gaussian curvature differences in this setting are dominated by single-voxel holes in the reference labels rather than by anatomy. We describe the evaluation protocol, the diagnostics, and their failure modes, and report preliminary hybrid-supervised population and meta-learned initialization experiments separately in an appendix.

2025

1. cmu-forum-2025

   Neural Ordinary Differential Equation based Spatiotemporal Glioblastoma Tumor Evolution Prediction

   Tushar Nayak, Cynthia Han, Kite Trenton, and 3 more authors

   Forum presentation, Carnegie Mellon Forum on Biomedical Engineering, 2025 · forum

   Video presentation

   Video
2. telesurge-review

   A Review of Robotics in Achieving Autonomous and Remote Neuroendovascular Intervention

   Rishi Basdeo, Sonali Patel, Tushar Nayak, and 5 more authors

   2025

   Under review at Journal of Intelligent & Robotic Systems, Springer Nature · under review

2024

1. lung-2024

   Automated histopathological detection and classification of lung cancer with an image pre-processing pipeline and spatial attention with deep neural networks

   Tushar Nayak, Nitila Gokulkrishnan, Krishnaraj Chadaga, and 3 more authors

   Cogent Engineering, 2024 · journal

   Abs DOI PDF

   Lung Cancer is a major cancer in the world and specifically India. Histopathological examination of tumorous tissue biopsy is the gold standard method used to clinically identify the type, sub-type, and stage of cancer. Two of the most prevalent forms of lung cancer: Adenocarcinoma & Squamous Cell Carcinoma account for nearly 80% of all lung cancer cases, which makes classifying the two subtypes of high importance. Proposed in this study is a data pre-processing pipeline for the H&E-stained lung biopsy images along with a customized EfficientNetB3-based Convolutional Neural Network employing spatial attention, trained on a public three-class lung cancer histopathological image dataset. The pre-processing pipeline employed before training, validation and testing helps enhance features of the histopathological images and removes biases due to stain variations for increased model robustness. The usage of a pre-trained CNN helps the deep learning model generalize better with the pre-trained weights, while the attention mechanism On three-fold validation, the classifier bagged accuracies of 0.9943 ± 0.0012 and 0.9947 ± 0.0018 and combined F1-Scores of 0.9942 ± 0.0042 and 0.9833 ± 0.0216 over the validation and testing data respectively. The high performance of the model combined with its computational efficiency could enable easy deployment of our model without necessitating infrastructure overhaul.

2. dengue-2024

   An explainable artificial intelligence integrated system for automatic detection of dengue from images of blood smears using transfer learning

   Hilda Mayrose, Niranjana Sampathila, G Muralidhar Bairy, and 3 more authors

   IEEE Access, 2024 · journal

   Abs DOI

   Dengue fever is a rapidly increasing mosquito-borne ailment spread by the virus DENV in the tropics and subtropics worldwide. It is a significant public health problem and accounts for many deaths globally. Implementing more effective methods that can more accurately detect dengue cases is challenging. The theme of this digital pathology-associated research is automatic dengue detection from peripheral blood smears (PBS) employing deep learning (DL) techniques. In recent years, DL has been significantly employed for automated computer-assisted diagnosis of various diseases from medical images. This paper explores pre-trained convolution neural networks (CNNs) for automatic dengue fever detection. Transfer learning (TL) is executed on three state-of-the-art CNNs – ResNet50, MobileNetV3Small, and MobileNetV3Large, to customize the models for differentiating the dengue-infected blood smears from the healthy ones. The dataset used to design and test the models contains 100x magnified dengue-infected and healthy control digital microscopic PBS images. The models are validated with a 5-fold cross-validation framework and tested on unseen data. An explainable artificial intelligence (XAI) approach, Gradient-weighted Class Activation Mapping (GradCAM), is eventually applied to the models to allow visualization of the precise regions on the smears most instrumental in making the predictions. While all three transferred pre-trained CNN models performed well (above 98% overall classification accuracy), MobileNetV3Small is the recommended model for this classification problem due to its significantly less computationally demanding characteristics. Transferred pre-trained CNN based on MobileNetV3Small yielded Accuracy, Recall, Specificity, Precision, F1 Score, and Area Under the ROC Curve (AUC) of 0.982 ± 0.011, 0.973 ± 0.027, 0.99 ± 0.013, 0.989 ± 0.015, 0.981 ± 0.012 and 0.982 ± 0.012 respectively, averaged over the five folds on the unseen dataset.

3. cmu-forum-2024

   Deep Learning Based Multi-Modal Multi-Stage Oral Cancer Detection: An Approach

   Tushar Nayak, Nitila Gokulkrishnan, and Niranjana Sampathila

   Forum presentation, Carnegie Mellon Forum on Biomedical Engineering, 2024 · forum

   Video presentation

   Video

2023

1. mpox-2023-binary

   Deep learning based detection of monkeypox virus using skin lesion images

   Tushar Nayak, Krishnaraj Chadaga, Niranjana Sampathila, and 5 more authors

   Medicine in Novel Technology and Devices, Jun 2023 · journal

   Abs DOI PDF

   As we set into the second half of 2022, the world is still recovering from the two-year COVID-19 pandemic. However, over the past three months, the outbreak of the Monkeypox Virus (MPV) has led to fifty-two thousand confirmed cases and over one hundred deaths. This caused the World Health Organisation to declare the outbreak a Public Health Emergency of International Concern (PHEIC). If this outbreak worsens, we could be looking at the Monkeypox virus causing the next global pandemic. As Monkeypox affects the human skin, the symptoms can be captured with regular imaging. Large samples of these images can be used as a training dataset for machine learning-based detection tools. Using a regular camera to capture the skin image of the infected person and running it against computer vision models is beneficial. In this research, we use deep learning to diagnose monkeypox from skin lesion images. Using a publicly available dataset, we tested the dataset on five pre-trained deep neural networks: GoogLeNet, Places365-GoogLeNet, SqueezeNet, AlexNet and ResNet-18. Hyperparameter was done to choose the best parameters. Performance metrics such as accuracy, precision, recall, f1-score and AUC were considered. Among the above models, ResNet18 was able to obtain the highest accuracy of 99.49%. The modified models obtained validation accuracies above 95%. The results prove that deep learning models such as the proposed model based on ResNet-18 can be deployed and can be crucial in battling the monkeypox virus. Since the used networks are optimized for efficiency, they can be used on performance limited devices such as smartphones with cameras. The addition of explainable artificial intelligence techniques LIME and GradCAM enables visual interpretation of the prediction made, helping health professionals using the model.

2. mpox-2023-multiclass

   Detection of Monkeypox from skin lesion images using deep learning networks and explainable artificial intelligence

   Tushar Nayak, Krishnaraj Chadaga, Niranjana Sampathila, and 5 more authors

   Applied Mathematics in Science and Engineering, Jun 2023 · journal

   Abs DOI PDF

   Monkeypox (Mpox) resurfaced in January 2022 as a rare zoonotic disease that spreads to many countries. Though the virus is not as dangerous as COVID-19, it has still caused many fatalities worldwide. The Mpox virus spreads when people are in close contact with infected individuals. Among many symptoms, the disease also causes skin rashes, and medical imaging can be used to diagnose the virus successfully. However, other diseases such as smallpox, chickenpox, and measles also cause similar skin rashes. Hence, artificial intelligence (AI) and machine learning (ML) can be highly beneficial in diagnosing Mpox from other similar diseases. After extensive model training, it is advantageous to use a standard camera to capture skin images of an infected patient and run it against deep learning (DL) models. In this research, we have used transfer learning models such as residual networks and SqueezeNet to diagnose Mpox from measles, chickenpox and healthy patients. An average accuracy of 91.19% and an F1-score of 92.55% were obtained for the Mpox class. The findings show that the models can be useful in detecting the contagious virus. Since the classifiers are easily deployable, they can be used on camera-ready devices such as phones and laptops.

3. lung-colon-2023

   Processing and Detection of Lung and Colon Cancer from Histopathological Images using Deep Residual Networks

   Tushar Nayak, Niranjana Sampathila, and Nitila Gokulkrishnan

   In 2023 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Jun 2023 · conference

   Abs DOI

   Lung & Colon cancer are amongst the leading cause of cancer related deaths worldwide. In this study, we used a five-class lung and colon cancer histopathology dataset and applied various image preprocessing techniques such as contrast stretching, unsharp masking, and resizing. We then trained a ResNetl01 model on this preprocessed dataset and achieved a high accuracy of 99.7%. The increased reliability and robustness of deep learning-based histopathology classifiers with high performance computers can enable instantaneous and automated diagnosis which can further help treatment and recovery.

4. dengue-2023

   Deep learning approach for detection of Dengue fever from the microscopic images of blood smear

   Hilda Mayrose, Niranjana Sampathila, G Muralidhar Bairy, and 3 more authors

   In Journal of Physics: Conference Series, Jun 2023 · conference

   Abs DOI

   Dengue virus (DENV), known to cause dengue fever is a global public health concern. A safe and effective anti-viral drug or vaccine that can protect humans from dengue fever currently does not exist. Today, severe dengue has become a leading cause of serious illness in most Asian and Latin American countries. This digital pathology-related research focuses on the automatic detection of dengue by utilizing digital microscopic peripheral blood smears (PBS). This paper explored pre-trained convolution neural network (CNN) architectures for automatic dengue fever detection. Transfer learning (TL) was performed on two widely used pre-trained CNNs - SqueezeNet and GoogleNet, and employed to differentiate the dengue-infected and normal blood smears. The last few layers were replaced and retrained to customize the architectures for this task. Leishman’s stained dengue-infected and normal control 100x magnified PBS images were included in the study. The best performance was rendered by GoogleNet (Learn Rate, 0.0001; Batch Size, 8) with an Accuracy 91.30%, Sensitivity 84.62%, Specificity 100%, Precision 100%, and F1 score 91.67%. Promising results show that this approach can be an essential adjunct to other clinical methods, namely CBC test & NS1 antigen capture, and can significantly support dengue diagnosis in low-resource setups.

5. leukemia-2023

   Deep learning-based analysis of blood smear images for detection of acute lymphoblastic leukemia

   Nitla Gokulkrishnan, Tushar Nayak, and Niranjana Sampathila

   In 2023 IEEE international conference on electronics, computing and communication technologies (CONECCT), Jun 2023 · conference
6. oral-cancer-2023

   Automated Oral Squamous Cell Carcinoma Detection from Histopathological Images Using Deep Neural Networks

   Tushar Nayak and Niranjana Sampathila

   Journal of Biomedical Engineering Society of India, Vol. 17, Jun 2023 · journal
7. lymphocytic-leukemia-2023

   Binary Detection of Acute Lymphocytic Leukemia Using Blood Smear Images Using Deep Learning Models

   Nitila Gokulkrishnan, Tushar Nayak, Niranjana Sampathila, and 2 more authors

   Journal of Biomedical Engineering Society of India, Vol. 17, Jun 2023 · journal