Search Results for author: Debdoot Sheet

Found 30 papers, 8 papers with code

Verifiable and Energy Efficient Medical Image Analysis with Quantised Self-attentive Deep Neural Networks

1 code implementation30 Sep 2022 Rakshith Sathish, Swanand Khare, Debdoot Sheet

Convolutional Neural Networks have played a significant role in various medical imaging tasks like classification and segmentation.

Classification Semantic Segmentation

Identification of Cervical Pathology using Adversarial Neural Networks

no code implementations28 Apr 2020 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.

Image Classification

A Two-Stage Multiple Instance Learning Framework for the Detection of Breast Cancer in Mammograms

no code implementations24 Apr 2020 Sarath Chandra K, Arunava Chakravarty, Nirmalya Ghosh, Tandra Sarkar, Ramanathan Sethuraman, Debdoot Sheet

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.

General Classification Multiple Instance Learning

CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation

1 code implementation17 Jan 2020 A. Emre Kavur, N. Sinem Gezer, Mustafa Barış, Sinem Aslan, Pierre-Henri Conze, Vladimir Groza, Duc Duy Pham, Soumick Chatterjee, Philipp Ernst, Savaş Özkan, Bora Baydar, Dmitry Lachinov, Shuo Han, Josef Pauli, Fabian Isensee, Matthias Perkonigg, Rachana Sathish, Ronnie Rajan, Debdoot Sheet, Gurbandurdy Dovletov, Oliver Speck, Andreas Nürnberger, Klaus H. Maier-Hein, Gözde BOZDAĞI AKAR, Gözde Ünal, Oğuz Dicle, M. Alper Selver

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).

Organ Segmentation

Adversarially Trained Convolutional Neural Networks for Semantic Segmentation of Ischaemic Stroke Lesion using Multisequence Magnetic Resonance Imaging

no code implementations3 Aug 2019 Rachana Sathish, Ronnie Rajan, Anusha Vupputuri, Nirmalya Ghosh, Debdoot Sheet

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.

Semantic Segmentation

Adversarially Trained Deep Neural Semantic Hashing Scheme for Subjective Search in Fashion Inventory

no code implementations30 Jun 2019 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.


Unit Impulse Response as an Explainer of Redundancy in a Deep Convolutional Neural Network

no code implementations10 Jun 2019 Rachana Sathish, Debdoot Sheet

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

no code implementations20 May 2019 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.

Multi-Task Learning

Fully Convolutional Neural Network for Semantic Segmentation of Anatomical Structure and Pathologies in Colour Fundus Images Associated with Diabetic Retinopathy

no code implementations7 Feb 2019 Oindrila Saha, Rachana Sathish, Debdoot Sheet

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.

Semantic Segmentation

SUMNet: Fully Convolutional Model for Fast Segmentation of Anatomical Structures in Ultrasound Volumes

1 code implementation21 Jan 2019 Sumanth Nandamuri, Debarghya China, Pabitra Mitra, Debdoot Sheet

Ultrasound imaging is generally employed for real-time investigation of internal anatomy of the human body for disease identification.

Anatomy Semantic Segmentation

Segmentation of Lumen and External Elastic Laminae in Intravascular Ultrasound Images using Ultrasonic Backscattering Physics Initialized Multiscale Random Walks

no code implementations21 Jan 2019 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 localizing vascular pathologies.

Learning a Deep Convolution Network with Turing Test Adversaries for Microscopy Image Super Resolution

no code implementations18 Jan 2019 Francis Tom, Himanshu Sharma, Dheeraj Mundhra, Tathagato Rai Dastidar, Debdoot Sheet

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.

Image Super-Resolution SSIM

Fully Convolutional Model for Variable Bit Length and Lossy High Density Compression of Mammograms

no code implementations17 May 2018 Aupendu Kar, Sri Phani Krishna Karri, Nirmalya Ghosh, Ramanathan Sethuraman, Debdoot Sheet

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.

Image Compression SSIM

Simulating Patho-realistic Ultrasound Images using Deep Generative Networks with Adversarial Learning

no code implementations21 Dec 2017 Francis Tom, Debdoot Sheet

We also quantify the shift in tissue specific intensity distributions of the real and simulated images to prove their similarity.

An Unsupervised Approach for Overlapping Cervical Cell Cytoplasm Segmentation

no code implementations17 Feb 2017 Pranav Kumar, S. L. Happy, Swarnadip Chatterjee, Debdoot Sheet, Aurobinda Routray

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

no code implementations19 Sep 2016 Avisek Lahiri, Abhijit Guha Roy, Debdoot Sheet, Prabir Kumar Biswas

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.

Retinal Vessel Segmentation

DASA: Domain Adaptation in Stacked Autoencoders using Systematic Dropout

no code implementations19 Mar 2016 Abhijit Guha Roy, Debdoot Sheet

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).

Domain Adaptation Retinal Vessel Segmentation

Ensemble of Deep Convolutional Neural Networks for Learning to Detect Retinal Vessels in Fundus Images

1 code implementation15 Mar 2016 Debapriya Maji, Anirban Santara, Pabitra Mitra, Debdoot Sheet

In this work we present a computational imaging framework using deep and ensemble learning for reliable detection of blood vessels in fundus color images.

Ensemble Learning Vessel Detection

Unsupervised Segmentation of Overlapping Cervical Cell Cytoplasm

no code implementations21 May 2015 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.

Cell Segmentation

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