Ph.D. (Tech.), in Computer Science and Engineering
26
Scopus Publications
3779
Scholar Citations
17
Scholar h-index
22
Scholar i10-index
Scopus Publications
Streamlining neuroradiology workflow with AI for improved cerebrovascular structure monitoring Subhashis Banerjee, Fredrik Nysjö, Dimitrios Toumpanakis, Ashis Kumar Dhara, Johan Wikström, et al. Scientific Reports, 2024 Radiological imaging to examine intracranial blood vessels is critical for preoperative planning and postoperative follow-up. Automated segmentation of cerebrovascular anatomy from Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) can provide radiologists with a more detailed and precise view of these vessels. This paper introduces a domain generalized artificial intelligence (AI) solution for volumetric monitoring of cerebrovascular structures from multi-center MRAs. Our approach utilizes a multi-task deep convolutional neural network (CNN) with a topology-aware loss function to learn voxel-wise segmentation of the cerebrovascular tree. We use Decorrelation Loss to achieve domain regularization for the encoder network and auxiliary tasks to provide additional regularization and enable the encoder to learn higher-level intermediate representations for improved performance. We compare our method to six state-of-the-art 3D vessel segmentation methods using retrospective TOF-MRA datasets from multiple private and public data sources scanned at six hospitals, with and without vascular pathologies. The proposed model achieved the best scores in all the qualitative performance measures. Furthermore, we have developed an AI-assisted Graphical User Interface (GUI) based on our research to assist radiologists in their daily work and establish a more efficient work process that saves time.
Lifelong Learning with Dynamic Convolutions for Glioma Segmentation from Multi-Modal MRI Subhashis Banerjee, Robin Strand Progress in Biomedical Optics and Imaging Proceedings of SPIE, 2023 This paper presents a novel solution for catastrophic forgetting in lifelong learning (LL) using Dynamic Convolution Neural Network (Dy-CNN). The proposed dynamic convolution layer can adapt convolution filters by learning kernel coefficients or weights based on the input image. The suitability of the proposed Dy-CNN in a lifelong sequential learning-based scenario with multi-modal MR images is experimentally demonstrated for the segmentation of Glioma tumors from multi-modal MR images. Experimental results demonstrated the superiority of the Dy-CNN-based segmenting network in terms of learning through multi-modal MRI images and better convergence of lifelong learning-based training.
Deep Active Learning for Glioblastoma Quantification Subhashis Banerjee, Robin Strand Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2023
Deep Curriculum Learning for Follow-up MRI Registration in Glioblastoma Subhashis Banerjee, Dimitrios Toumpanakis, Ashis K. Dhara, Johan Wikström, Robin Strand Progress in Biomedical Optics and Imaging Proceedings of SPIE, 2023 This paper presents a weakly supervised deep convolutional neural network-based approach to perform voxel-level 3D registration between subsequent follow-up MRI scans of the same patient. To handle the large deformation in the surrounding brain tissues due to the tumor’s mass effect we proposed curriculum learning-based training for the network. Weak supervision helps the network to concentrate more focus on the tumor region and resection cavity through a saliency detection network. Qualitative and quantitative experimental results show the proposed registration network outperformed two popular state-of-the-art methods.
Topology-Aware Learning for Volumetric Cerebrovascular Segmentation Subhashis Banerjee, Dimitrios Toumpanakis, Ashis Kumar Dhara, Johan Wikstrom, Robin Strand Proceedings International Symposium on Biomedical Imaging, 2022 This paper presents a topology-aware learning strategy for volumetric segmentation of intracranial cerebrovascular structures. We propose a multi-task deep CNN along with a topology-aware loss function for this purpose. Along with the main task (i.e. segmentation), we train the model to learn two related auxiliary tasks viz. learning the distance transform for the voxels on the surface of the vascular tree and learning the vessel centerline. This provides additional regularization and allows the encoder to learn higher-level intermediate representations to boost the performance of the main task. We compare the proposed method with six state-of-the-art deep learning-based 3D vessel segmentation methods, by using a public Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) dataset. Experimental results demonstrate that the proposed method has the best performance in this particular context.
Segmentation of Major Cerebral Vessel from MRA images and Evaluation using U-Net Family Subhash Chandra Pal, Subhashis Banerjee, Dimitrios Toumpanakis, Johan Wikström, Robin Strand, et al. Proceedings of 2022 6th International Conference on Condition Assessment Techniques in Electrical Systems Catcon 2022, 2022 Arterial cerebral vessel assessment is critical for the diagnosis of patients with cerebrovascular disease e.g., hypertension, Intracranial aneurysms, and dementia. Magnetic resonance angiography is a primary imaging technique for diagnosing cerebrovascular diseases. There are many Convolutional neural networks (CNN) based methods for cerebral vessel segmentation but lack to identify the target vessels and understand the arterial tree structure for diagnosis and endovascular surgical planning. In the present study, we generated annotations for major vessel segmentation and analyzed fully automatic segmentation of major vessels using state-of-the-art U-Net based deep learning models. Computer-aided major cerebral vessel segmentation incorporated into clinical practice may help speed up the diagnosis of time-critical vessel anomalies and help find important bio-markers for neurological dysfunction. We validated and compared U-Net based models for volumetric segmentation and prediction of cerebral arteries and it could be done in real-time without any image pre-processing.
Deepsgp:Deep learning for gene selection and survival group prediction in glioblastoma Ritaban Kirtania, Subhashis Banerjee, Sayantan Laha, B. Uma Shankar, Raghunath Chatterjee, et al. Electronics Switzerland, 2021 Glioblastoma Multiforme (GBM) is an aggressive form of glioma, exhibiting very poor survival. Genomic input, in the form of RNA sequencing data (RNA-seq), is expected to provide vital information about the characteristics of the genes that affect the Overall Survival (OS) of patients. This could have a significant impact on treatment planning. We present a new Autoencoder (AE)-based strategy for the prediction of survival (low or high) of GBM patients, using the RNA-seq data of 129 GBM samples from The Cancer Genome Atlas (TCGA). This is a novel interdisciplinary approach to integrating genomics with deep learning towards survival prediction. First, the Differentially Expressed Genes (DEGs) were selected using EdgeR. These were further reduced using correlation-based analysis. This was followed by the application of ranking with different feature subset selection and feature extraction algorithms, including the AE. In each case, fifty features were selected/extracted, for subsequent prediction with different classifiers. An exhaustive study for survival group prediction, using eight different classifiers with the accuracy and Area Under the Curve (AUC), established the superiority of the AE-based feature extraction method, called DeepSGP. It produced a very high accuracy (0.83) and AUC (0.90). Of the eight classifiers, using the extracted features by DeepSGP, the MLP was the best at Overall Survival (OS) prediction with an accuracy of 0.89 and an AUC of 0.97. The biological significance of the genes extracted by the AE were also analyzed to establish their importance. Finally, the statistical significance of the predicted output of the DeepSGP algorithm was established using the concordance index.
An Explainable AI-based Motion Detection approach for MR images without requirement of motion annotated ground truth data S Banerjee, D Shanbhag, S Chatterjee ISMRM & ISMRT , 2025 2025
Inter-Frame distance metric-based Auto-Inversion-Time prediction for Cardiac MR S Banerjee, S Chatterjee, G Delso, S Rajamani, M Janich, D Shanbhag ISMRM & ISMRT , 2025 2025
Streamlining neuroradiology workflow with AI for improved cerebrovascular structure monitoring S Banerjee, F Nysjö, D Toumpanakis, AK Dhara, J Wikström, R Strand Scientific Reports 14 (1), 9245 , 2024 2024 Citations: 10
3-D Attention-SEV-Net for Segmentation of Post-operative Glioblastoma with Interactive Correction of Over-Segmentation S Kundu, S Banerjee, D Toumpanakis, J Wikstrom, R Strand, AK Dhara International Conference on Pattern Recognition and Machine Intelligence … , 2023 2023 Citations: 4
Deep Active Learning for Glioblastoma Quantification S Banerjee, R Strand Image Analysis: 23rd Scandinavian Conference, SCIA 2023, Sirkka, Finland … , 2023 2023
AI-based solution for improving neuroradiology workflow for cerebrovascular structure monitoring S Banerjee, F Nysjö, AK Dhara, J Wikström, R Strand 2023 Citations: 3
ASE-Net for Segmentation of Post-operative Glioblastoma and Patient-specific Fine-tuning for Segmentation Refinement of Follow-up MRI Scans S Kundu, S Banerjee, E Breznik, D Toumpanakis, J Wikström, R Strand, ... 2023 Citations: 6
Deep Curriculum Learning for Follow-up MRI Registration in Glioblastoma S Banerjee, D Toumpanakis, A Dhara, J Wikström, R Strand SPIE Mecial imaging 2023 , 2023 2023
Lifelong Learning with Dynamic Convolutions for Glioma: Segmentation from Multi-Modal MRI S Banerjee, R Strand SPIE Medical Imaging 2023 , 2023 2023 Citations: 3
Segmentation of Major Cerebral Vessel from MRA images and Evaluation using U-Net Family SC Pal, S Banerjee, D Toumpanakis, J Wikström, R Strand, AK Dhara 2022 IEEE 6th International Conference on Condition Assessment Techniques in … , 2022 2022 Citations: 9
Topology-Aware Learning for Volumetric Cerebrovascular Segmentation S Banerjee, D Toumpanakis, AK Dhara, J Wikström, R Strand 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), 1-4 , 2022 2022 Citations: 27
QU-BraTS: MICCAI BraTS 2020 Challenge on QuantifyingUncertainty in Brain Tumor Segmentation-Analysis of Ranking Scores and Benchmarking Results R Mehta, A Filos, U Baid, C Sako, R McKinley, M Rebsamen, K Dätwyler, ... Journal of Machine Learning for Biomedical Imaging 1 , 2022 2022 Citations: 103
Deep learning for noninvasive management of brain tumors S Banerjee, S Mitra Augmenting Neurological Disorder Prediction and Rehabilitation Using … , 2022 2022
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation--Analysis of Ranking Metrics and Benchmarking Results R Mehta, A Filos, U Baid, C Sako, R McKinley, M Rebsamen, K Dätwyler, ... arXiv preprint arXiv:2112.10074 , 2021 2021
Analysis of MRI Biomarkers for Brain Cancer Survival Prediction S Banerjee, S Mitra, LO Hall arXiv preprint arXiv:2109.02785 , 2021 2021 Citations: 3
DeepSGP: Deep Learning for Gene Selection and Survival Group Prediction in Glioblastoma R Kirtania, S Banerjee, S Laha, BU Shankar, R Chatterjee, S Mitra Electronics 10 (12), 1463 , 2021 2021 Citations: 5
Segmentation of Intracranial Aneurysm Remnant in MRA using Dual-Attention Atrous Net S Banerjee, A Kumar Dhara, J Wikström, R Strand 25th International Conference on Pattern Recognition (ICPR 2020), Milano , 2021 2021 Citations: 6
Iris segmentation using interactive deep learning M Sardar, S Banerjee, S Mitra IEEE Access 8, 219322-219330 , 2020 2020 Citations: 49
Evolving Optimal Convolutional Neural Networks S Banerjee, S Mitra 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2677-2683 , 2020 2020 Citations: 2
Glioma Classification Using Deep Radiomics S Banerjee, S Mitra, F Masulli, S Rovetta SN Computer Science 1 (4), 1-14 , 2020 2020 Citations: 32
MOST CITED SCHOLAR PUBLICATIONS
Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ... arXiv preprint arXiv:1811.02629 , 2018 2018 Citations: 2828
QU-BraTS: MICCAI BraTS 2020 Challenge on QuantifyingUncertainty in Brain Tumor Segmentation-Analysis of Ranking Scores and Benchmarking Results R Mehta, A Filos, U Baid, C Sako, R McKinley, M Rebsamen, K Dätwyler, ... Journal of Machine Learning for Biomedical Imaging 1 , 2022 2022 Citations: 103
Deep radiomics for brain tumor detection and classification from multi-sequence MRI S Banerjee, S Mitra, F Masulli, S Rovetta arXiv preprint arXiv:1903.09240 , 2019 2019 Citations: 79
A novel GBM saliency detection model using multi-channel MRI S Banerjee, S Mitra, BU Shankar, Y Hayashi PloS one 11 (1), e0146388 , 2016 2016 Citations: 65
Automated 3D segmentation of brain tumor using visual saliency S Banerjee, S Mitra, BU Shankar Information Sciences 424, 337-353 , 2018 2018 Citations: 63
Single seed delineation of brain tumor using multi-thresholding S Banerjee, S Mitra, BU Shankar Information Sciences 330, 88-103 , 2016 2016 Citations: 63
Multi-planar spatial-ConvNet for segmentation and survival prediction in brain cancer S Banerjee, S Mitra, BU Shankar International MICCAI Brainlesion Workshop, 94-104 , 2018 2018 Citations: 52
A Survey on Applications of Siamese Neural Networks in Computer Vision A Nandy, S Haldar, S Banerjee, S Mitra 2020 International Conference for Emerging Technology (INCET), 1-5 , 2020 2020 Citations: 51
Iris segmentation using interactive deep learning M Sardar, S Banerjee, S Mitra IEEE Access 8, 219322-219330 , 2020 2020 Citations: 49
GAN-based novel approach for data augmentation with improved disease classification D Bhattacharya, S Banerjee, S Bhattacharya, B Uma Shankar, S Mitra Advancement of Machine Intelligence in Interactive Medical Image Analysis … , 2020 2020 Citations: 42
Brain tumor detection and classification from multi-sequence MRI: study using convnets S Banerjee, S Mitra, F Masulli, S Rovetta International MICCAI Brainlesion Workshop, 170-179 , 2018 2018 Citations: 41
Volumetric brain tumour detection from MRI using visual saliency S Mitra, S Banerjee, Y Hayashi PloS one 12 (11), e0187209 , 2017 2017 Citations: 36
Novel Volumetric Sub-region Segmentation in Brain Tumors S Banerjee, S Mitra Frontiers in Computational Neuroscience 14, 3 , 2020 2020 Citations: 35
Glioma Classification Using Deep Radiomics S Banerjee, S Mitra, F Masulli, S Rovetta SN Computer Science 1 (4), 1-14 , 2020 2020 Citations: 32
Synergetic neuro-fuzzy feature selection and classification of brain tumors S Banerjee, S Mitra, BU Shankar 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1-6 , 2017 2017 Citations: 32
Topology-Aware Learning for Volumetric Cerebrovascular Segmentation S Banerjee, D Toumpanakis, AK Dhara, J Wikström, R Strand 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), 1-4 , 2022 2022 Citations: 27
Aodv based black-hole attack mitigation in manet S Banerjee, M Sardar, K Majumder Proceedings of the international conference on frontiers of intelligent … , 2014 2014 Citations: 21
On an optimization technique using binary decision diagram D Sensarma, S Banerjee, K Basuli, S Naskar, SS Sarma arXiv preprint arXiv:1203.2505 , 2012 2012 Citations: 16
A CADe system for gliomas in brain MRI using convolutional neural networks S Banerjee, S Mitra, A Sharma, BU Shankar arXiv preprint arXiv:1806.07589 , 2018 2018 Citations: 15
Brain Tumor Detection and Classification from Multi-Channel MRIs using Deep Learning and Transfer Learning S Banerjee, F Masulli, S Mitra https://cis.ieee.org/images/files/Documents/GSRG/2017 … , 2018 2018 Citations: 15
