Search Results for author:
Found 32 papers, 8 papers with code
SERPENT-VLM : Self-Refining Radiology Report Generation Using Vision Language Models
Radiology Report Generation (R2Gen) demonstrates how Multi-modal Large Language Models (MLLMs) can automate the creation of accurate and coherent radiological reports.
Knowledge Distillation of Convolutional Neural Networks through Feature Map Transformation using Decision Trees
We propose a distillation approach by extracting features from the final layer of the convolutional neural network (CNN) to address insights to its reasoning.
Exploiting Richness of Learned Compressed Representation of Images for Semantic Segmentation
Autonomous vehicles and Advanced Driving Assistance Systems (ADAS) have the potential to radically change the way we travel.
CholecTriplet2022: Show me a tool and tell me the triplet -- an endoscopic vision challenge for surgical action triplet detection
This paper presents the CholecTriplet2022 challenge, which extends surgical action triplet modeling from recognition to detection.
Ranked #1 on
Action Triplet Detection
on CholecT50 (Challenge)
Verifiable and Energy Efficient Medical Image Analysis with Quantised Self-attentive Deep Neural Networks
Convolutional Neural Networks have played a significant role in various medical imaging tasks like classification and segmentation.
CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
In this paper, we present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge.
Ranked #1 on
Action Triplet Recognition
on CholecT50 (Challenge)
(using extra training data)
Lung Segmentation and Nodule Detection in Computed Tomography Scan using a Convolutional Neural Network Trained Adversarially using Turing Test Loss
Lung cancer is the most common form of cancer found worldwide with a high mortality rate.
Identification of Cervical Pathology using Adversarial Neural Networks
Various screening and diagnostic methods have led to a large reduction of cervical cancer death rates in developed countries.
IROS 2019 Lifelong Robotic Vision Challenge -- Lifelong Object Recognition Report
This report summarizes IROS 2019-Lifelong Robotic Vision Competition (Lifelong Object Recognition Challenge) with methods and results from the top $8$ finalists (out of over~$150$ teams).
A Two-Stage Multiple Instance Learning Framework for the Detection of Breast Cancer in Mammograms
Our method performed well on the task of localization of masses with an average Precision/Recall of 0. 76/0. 80 and acheived an average AUC of 0. 91 on the imagelevel classification task using a five-fold cross-validation on the INbreast dataset.
A Systematic Search over Deep Convolutional Neural Network Architectures for Screening Chest Radiographs
Chest radiographs are primarily employed for the screening of pulmonary and cardio-/thoracic conditions.
Learning Decision Ensemble using a Graph Neural Network for Comorbidity Aware Chest Radiograph Screening
Chest radiographs are primarily employed for the screening of cardio, thoracic and pulmonary conditions.
CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation
The analysis shows that the performance of DL models for single modality (CT / MR) can show reliable volumetric analysis performance (DICE: 0. 98 $\pm$ 0. 00 / 0. 95 $\pm$ 0. 01) but the best MSSD performance remain limited (21. 89 $\pm$ 13. 94 / 20. 85 $\pm$ 10. 63 mm).
Adversarially Trained Convolutional Neural Networks for Semantic Segmentation of Ischaemic Stroke Lesion using Multisequence Magnetic Resonance Imaging
In this paper, we present a data-driven fully automated method for estimation of core and penumbra in ischaemic lesions using diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) sequence maps of MRI.
Adversarially Trained Deep Neural Semantic Hashing Scheme for Subjective Search in Fashion Inventory
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.
Unit Impulse Response as an Explainer of Redundancy in a Deep Convolutional Neural Network
Convolutional neural networks (CNN) are generally designed with a heuristic initialization of network architecture and trained for a certain task.
Multitask Learning of Temporal Connectionism in Convolutional Networks using a Joint Distribution Loss Function to Simultaneously Identify Tools and Phase in Surgical Videos
Surgical workflow analysis is of importance for understanding onset and persistence of surgical phases and individual tool usage across surgery and in each phase.
Fully Convolutional Neural Network for Semantic Segmentation of Anatomical Structure and Pathologies in Colour Fundus Images Associated with Diabetic Retinopathy
This paper proposes a method for the automated segmentation of retinal lesions and optic disk in fundus images using a deep fully convolutional neural network for semantic segmentation.
Segmentation of Lumen and External Elastic Laminae in Intravascular Ultrasound Images using Ultrasonic Backscattering Physics Initialized Multiscale Random Walks
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 localizing vascular pathologies.
Learning a Deep Convolution Network with Turing Test Adversaries for Microscopy Image Super Resolution
Adversarially trained deep neural networks have significantly improved performance of single image super resolution, by hallucinating photorealistic local textures, thereby greatly reducing the perception difference between a real high resolution image and its super resolved (SR) counterpart.
UltraCompression: Framework for High Density Compression of Ultrasound Volumes using Physics Modeling Deep Neural Networks
Ultrasound image compression by preserving speckle-based key information is a challenging task.
Jointly Learning Convolutional Representations to Compress Radiological Images and Classify Thoracic Diseases in the Compressed Domain
Deep learning models trained in natural images are commonly used for different classification tasks in the medical domain.
Ranked #5 on
Pneumonia Detection
on ChestX-ray14
Fully Convolutional Model for Variable Bit Length and Lossy High Density Compression of Mammograms
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.
Simulating Patho-realistic Ultrasound Images using Deep Generative Networks with Adversarial Learning
We also quantify the shift in tissue specific intensity distributions of the real and simulated images to prove their similarity.
Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data
While large datasets of unlabeled image data are available in medical applications, access to manually labeled data is very limited.
ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Network
Optical coherence tomography (OCT) is used for non-invasive diagnosis of diabetic macular edema assessing the retinal layers.
An Unsupervised Approach for Overlapping Cervical Cell Cytoplasm Segmentation
The poor contrast and the overlapping of cervical cell cytoplasm are the major issues in the accurate segmentation of cervical cell cytoplasm.
Deep Neural Ensemble for Retinal Vessel Segmentation in Fundus Images towards Achieving Label-free Angiography
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, hypertensive disorders and cardiovascular diseases.
DASA: Domain Adaptation in Stacked Autoencoders using Systematic Dropout
We experimentally evaluate performance in the problem of retinal vessel segmentation where the SAE-DNN is trained using large number of labeled samples in the source domain (DRIVE dataset) and adapted using less number of labeled samples in target domain (STARE dataset).
Ensemble of Deep Convolutional Neural Networks for Learning to Detect Retinal Vessels in Fundus Images
In this work we present a computational imaging framework using deep and ensemble learning for reliable detection of blood vessels in fundus color images.
Unsupervised Segmentation of Overlapping Cervical Cell Cytoplasm
Overlapping of cervical cells and poor contrast of cell cytoplasm are the major issues in accurate detection and segmentation of cervical cells.
