Deep Learning Radiomics Algorithm for Gliomas (DRAG) Model: A Novel Approach Using 3D UNET Based Deep Convolutional Neural Network for Predicting Survival in Gliomas. - Ujjwal Baid; Sanjay N. Talbar; Swapnil Rane; Sudeep Gupta; Meenakshi H. Thakur; Aliasgar Moiyadi; Siddhesh Thakur; Abhishek Mahajan (BrainLes@MICCAI (2), 2018)

Overall Survival Prediction in Glioblastoma With Radiomic Features Using Machine Learning. - Ujjwal Baid; Swapnil Rane; Sanjay N. Talbar; Sudeep Gupta; Meenakshi H. Thakur; Aliasgar Moiyadi; Abhishek Mahajan (Frontiers Comput. Neurosci., 2020)

A Novel Approach for Fully Automatic Intra-Tumor Segmentation With 3D U-Net Architecture for Gliomas. - Ujjwal Baid; Sanjay N. Talbar; Swapnil Rane; Sudeep Gupta; Meenakshi H. Thakur; Aliasgar Moiyadi; Nilesh Sable; Mayuresh Akolkar; Abhishek Mahajan (Frontiers Comput. Neurosci., 2020)

Robust Classification of Histology Images Exploiting Adversarial Auto Encoders. - Nikhil Cherian Kurian; Gurparkash Singh; Poorvi Hebbar; Shreekanya Kodate; Swapnil Rane; Amit Sethi (EMBC, 2021)

MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge. - Ruchika Verma; Neeraj Kumar 002; Abhijeet Patil; Nikhil Cherian Kurian; Swapnil Rane; Simon Graham; Quoc Dang Vu; Mieke Zwager; Shan-E-Ahmed Raza; Nasir M. Rajpoot; Xiyi Wu; Huai Chen; et al. (IEEE Trans. Medical Imaging, 2021)

A 2021 update on cancer image analytics with deep learning. - Nikhil Cherian Kurian; Amit Sethi; Anil Reddy Konduru; Abhishek Mahajan; Swapnil Ulhas Rane (WIREs Data Mining Knowl. Discov., 2021)

Deep Multi-Scale U-Net Architecture and Label-Noise Robust Training Strategies for Histopathological Image Segmentation. - Nikhil Cherian Kurian; Amit Lehan; Gregory Verghese; Nimish Dharamshi; Swati Meena; Mengyuan Li; Fangfang Liu; Cheryl Gillet; Swapnil Rane; Anita Grigoriadis; Amit Sethi (BIBE, 2022)

Deep Multi-Scale U-Net Architecture and Noise-Robust Training Strategies for Histopathological Image Segmentation. - Nikhil Cherian Kurian; Amit Lohan; Gregory Verghese; Nimish Dharamshi; Swati Meena; Mengyuan Li; Fangfang Liu; Cheryl Gillet; Swapnil Rane; Anita Grigoriadis; Amit Sethi (CoRR, 2022)

Author's Reply to "MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge". - Ruchika Verma; Neeraj Kumar 002; Abhijeet Patil; Nikhil Cherian Kurian; Swapnil Rane; Amit Sethi (IEEE Trans. Medical Imaging, 2022)

EGFR Mutation Prediction of Lung Biopsy Images Using Deep Learning. - Ravi Kant Gupta; Shivani Nandgaonkar; Nikhil Cherian Kurian; Tripti Bameta; Subhash Yadav; Rajiv Kumar Kaushal; Swapnil Rane; Amit Sethi (BIOIMAGING, 2023)

HER2 and Fish Status Prediction in Breast Biopsy H&E-Stained Images Using Deep Learning. - Ardhendu Sekhar; Vrinda Goel; Garima Jain; Abhijeet Patil; Ravi Kant Gupta; Tripti Bameta; Swapnil Rane; Amit Sethi (BIBE, 2024)

Combining Datasets with Different Label Sets for Improved Nucleus Segmentation and Classification. - Amruta Parulekar; Utkarsh Kanwat; Ravi Kant Gupta; Medha Chippa; Thomas Jacob; Tripti Bameta; Swapnil Rane; Amit Sethi (BIOSTEC (1), 2024)

Efficient Quality Control of Whole Slide Pathology Images with Human-in-the-loop Training. - Abhijeet Patil; Harsh Diwakar; Jay Sawant; Nikhil Cherian Kurian; Subhash Yadav; Swapnil Rane; Tripti Bameta; Amit Sethi (CoRR, 2024)

Semantic Segmentation Based Quality Control of Histopathology Whole Slide Images. - Abhijeet Patil; Garima Jain; Harsh Diwakar; Jay Sawant; Tripti Bameta; Swapnil Rane; Amit Sethi (CoRR, 2024)

HER2 Status Prediction in Breast Cancer: A Weak Supervision and Contrastive Learning Approach on H&E Stains. - Ardhendu Sekhar; Vrinda Goel; Garima Jain; Abhijeet Patil; Aditya Bhangale; Ravi Kant Gupta; Tripti Bameta; Swapnil Rane; Amit Sethi (ISBI, 2025)