Publications (32)
SUMNet: Fully Convolutional Model for Fast Segmentation of Anatomical Structures in Ultrasound Volumes
Sumanth Nandamuri, Debarghya China, Pabitra Mitra +1
Ultrasound imaging is generally employed for real-time investigation of internal anatomy of the human body for disease identification. Delineation of the anatomical boundary of org…
Segmentation of Lumen and External Elastic Laminae in Intravascular Ultrasound Images using Ultrasonic Backscattering Physics Initialized Multiscale Random Walks
Debarghya China, Pabitra Mitra, Debdoot Sheet
Coronary artery disease accounts for a large number of deaths across the world and clinicians generally prefer using x-ray computed tomography or magnetic resonance imaging for loc…
SERPENT-VLM : Self-Refining Radiology Report Generation Using Vision Language Models
Manav Nitin Kapadnis, Sohan Patnaik, Abhilash Nandy +3
Radiology Report Generation (R2Gen) demonstrates how Multi-modal Large Language Models (MLLMs) can automate the creation of accurate and coherent radiological reports. Existing met…
Fully Convolutional Model for Variable Bit Length and Lossy High Density Compression of Mammograms
Aupendu Kar, Sri Phani Krishna Karri, Nirmalya Ghosh +2
Early works on medical image compression date to the 1980's with the impetus on deployment of teleradiology systems for high-resolution digital X-ray detectors. Commercially deploy…
Deep Neural Ensemble for Retinal Vessel Segmentation in Fundus Images towards Achieving Label-free Angiography
Avisek Lahiri, Abhijit Guha Roy, Debdoot Sheet +1
Automated segmentation of retinal blood vessels in label-free fundus images entails a pivotal role in computed aided diagnosis of ophthalmic pathologies, viz., diabetic retinopathy…
An Unsupervised Approach for Overlapping Cervical Cell Cytoplasm Segmentation
Pranav Kumar, S L Happy, Swarnadip Chatterjee +2
The poor contrast and the overlapping of cervical cell cytoplasm are the major issues in the accurate segmentation of cervical cell cytoplasm. This paper presents an automated unsu…
Learning Decision Ensemble using a Graph Neural Network for Comorbidity Aware Chest Radiograph Screening
Arunava Chakravarty, Tandra Sarkar, Nirmalya Ghosh +2
Chest radiographs are primarily employed for the screening of cardio, thoracic and pulmonary conditions. Machine learning based automated solutions are being developed to reduce th…
Adversarially Trained Convolutional Neural Networks for Semantic Segmentation of Ischaemic Stroke Lesion using Multisequence Magnetic Resonance Imaging
Rachana Sathish, Ronnie Rajan, Anusha Vupputuri +2
Ischaemic stroke is a medical condition caused by occlusion of blood supply to the brain tissue thus forming a lesion. A lesion is zoned into a core associated with irreversible ne…
DASA: Domain Adaptation in Stacked Autoencoders using Systematic Dropout
Abhijit Guha Roy, Debdoot Sheet
Domain adaptation deals with adapting behaviour of machine learning based systems trained using samples in source domain to their deployment in target domain where the statistics o…
Identification of Cervical Pathology using Adversarial Neural Networks
Abhilash Nandy, Rachana Sathish, Debdoot Sheet
Various screening and diagnostic methods have led to a large reduction of cervical cancer death rates in developed countries. However, cervical cancer is the leading cause of cance…
Multitask Learning of Temporal Connectionism in Convolutional Networks using a Joint Distribution Loss Function to Simultaneously Identify Tools and Phase in Surgical Videos
Shanka Subhra Mondal, Rachana Sathish, Debdoot Sheet
Surgical workflow analysis is of importance for understanding onset and persistence of surgical phases and individual tool usage across surgery and in each phase. It is beneficial…
Verifiable and Energy Efficient Medical Image Analysis with Quantised Self-attentive Deep Neural Networks
Rakshith Sathish, Swanand Khare, Debdoot Sheet
Convolutional Neural Networks have played a significant role in various medical imaging tasks like classification and segmentation. They provide state-of-the-art performance compar…
CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
Chinedu Innocent Nwoye, Deepak Alapatt, Tong Yu +59
Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works…
Learning a Deep Convolution Network with Turing Test Adversaries for Microscopy Image Super Resolution
Francis Tom, Himanshu Sharma, Dheeraj Mundhra +2
Adversarially trained deep neural networks have significantly improved performance of single image super resolution, by hallucinating photorealistic local textures, thereby greatly…
Exploiting Richness of Learned Compressed Representation of Images for Semantic Segmentation
Ravi Kakaiya, Rakshith Sathish, Ramanathan Sethuraman +1
Autonomous vehicles and Advanced Driving Assistance Systems (ADAS) have the potential to radically change the way we travel. Many such vehicles currently rely on segmentation and o…
CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation
A. Emre Kavur, N. Sinem Gezer, Mustafa BarıŠ+24
Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) have intr…
Unit Impulse Response as an Explainer of Redundancy in a Deep Convolutional Neural Network
Rachana Sathish, Debdoot Sheet
Convolutional neural networks (CNN) are generally designed with a heuristic initialization of network architecture and trained for a certain task. This often leads to overparametri…
Ensemble of Deep Convolutional Neural Networks for Learning to Detect Retinal Vessels in Fundus Images
Debapriya Maji, Anirban Santara, Pabitra Mitra +1
Vision impairment due to pathological damage of the retina can largely be prevented through periodic screening using fundus color imaging. However the challenge with large scale sc…
Significance of Residual Learning and Boundary Weighted Loss in Ischaemic Stroke Lesion Segmentation
Ronnie Rajan, Rachana Sathish, Debdoot Sheet
Radiologists use various imaging modalities to aid in different tasks like diagnosis of disease, lesion visualization, surgical planning and prognostic evaluation. Most of these ta…
Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data
Abhijit Guha Roy, Sailesh Conjeti, Debdoot Sheet +3
Training deep fully convolutional neural networks (F-CNNs) for semantic image segmentation requires access to abundant labeled data. While large datasets of unlabeled image data ar…
Fully Convolutional Neural Network for Semantic Segmentation of Anatomical Structure and Pathologies in Colour Fundus Images Associated with Diabetic Retinopathy
Oindrila Saha, Rachana Sathish, Debdoot Sheet
Diabetic retinopathy (DR) is the most common form of diabetic eye disease. Retinopathy can affect all diabetic patients and becomes particularly dangerous, increasing the risk of b…
Knowledge Distillation of Convolutional Neural Networks through Feature Map Transformation using Decision Trees
Maddimsetti Srinivas, Debdoot Sheet
The interpretation of reasoning by Deep Neural Networks (DNN) is still challenging due to their perceived black-box nature. Therefore, deploying DNNs in several real-world tasks is…
ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Network
Abhijit Guha Roy, Sailesh Conjeti, Sri Phani Krishna Karri +4
Optical coherence tomography (OCT) is used for non-invasive diagnosis of diabetic macular edema assessing the retinal layers. In this paper, we propose a new fully convolutional de…
Adversarially Trained Deep Neural Semantic Hashing Scheme for Subjective Search in Fashion Inventory
Saket Singh, Debdoot Sheet, Mithun Dasgupta
The simple approach of retrieving a closest match of a query image from one in the gallery, compares an image pair using sum of absolute difference in pixel or feature space. The p…
UltraCompression: Framework for High Density Compression of Ultrasound Volumes using Physics Modeling Deep Neural Networks
Debarghya China, Francis Tom, Sumanth Nandamuri +4
Ultrasound image compression by preserving speckle-based key information is a challenging task. In this paper, we introduce an ultrasound image compression framework with the abili…
Lung Segmentation and Nodule Detection in Computed Tomography Scan using a Convolutional Neural Network Trained Adversarially using Turing Test Loss
Rakshith Sathish, Rachana Sathish, Ramanathan Sethuraman +1
Lung cancer is the most common form of cancer found worldwide with a high mortality rate. Early detection of pulmonary nodules by screening with a low-dose computed tomography (CT)…
A Two-Stage Multiple Instance Learning Framework for the Detection of Breast Cancer in Mammograms
Sarath Chandra K, Arunava Chakravarty, Nirmalya Ghosh +3
Mammograms are commonly employed in the large scale screening of breast cancer which is primarily characterized by the presence of malignant masses. However, automated image-level…
CholecTriplet2022: Show me a tool and tell me the triplet -- an endoscopic vision challenge for surgical action triplet detection
Chinedu Innocent Nwoye, Tong Yu, Saurav Sharma +46
Formalizing surgical activities as triplets of the used instruments, actions performed, and target anatomies is becoming a gold standard approach for surgical activity modeling. Th…
Simulating Patho-realistic Ultrasound Images using Deep Generative Networks with Adversarial Learning
Francis Tom, Debdoot Sheet
Ultrasound imaging makes use of backscattering of waves during their interaction with scatterers present in biological tissues. Simulation of synthetic ultrasound images is a chall…
Unsupervised Segmentation of Overlapping Cervical Cell Cytoplasm
S L Happy, Swarnadip Chatterjee, Debdoot Sheet
Overlapping of cervical cells and poor contrast of cell cytoplasm are the major issues in accurate detection and segmentation of cervical cells. An unsupervised cell segmentation a…
IROS 2019 Lifelong Robotic Vision Challenge -- Lifelong Object Recognition Report
Qi She, Fan Feng, Qi Liu +33
This report summarizes IROS 2019-Lifelong Robotic Vision Competition (Lifelong Object Recognition Challenge) with methods and results from the top finalists (out of over~…
A Systematic Search over Deep Convolutional Neural Network Architectures for Screening Chest Radiographs
Arka Mitra, Arunava Chakravarty, Nirmalya Ghosh +3
Chest radiographs are primarily employed for the screening of pulmonary and cardio-/thoracic conditions. Being undertaken at primary healthcare centers, they require the presence o…