Search Results for author: Debesh Jha

Found 38 papers, 20 papers with code

RUPNet: Residual upsampling network for real-time polyp segmentation

no code implementations6 Jan 2023 Nikhil Kumar Tomar, Ulas Bagci, Debesh Jha

Here, we propose a novel architecture, Residual Upsampling Network (RUPNet) for colon polyp segmentation that can process in real-time and show high recall and precision.

Domain Generalization with Correlated Style Uncertainty

no code implementations20 Dec 2022 Zheyuan Zhang, Bin Wang, Debesh Jha, Ugur Demir, Ulas Bagci

Hence, increasing the source domain diversity is a key component of domain generalization.

Domain Generalization Retrieval

A Critical Appraisal of Data Augmentation Methods for Imaging-Based Medical Diagnosis Applications

no code implementations14 Dec 2022 Tara M. Pattilachan, Ugur Demir, Elif Keles, Debesh Jha, Derk Klatte, Megan Engels, Sanne Hoogenboom, Candice Bolan, Michael Wallace, Ulas Bagci

Current data augmentation techniques and transformations are well suited for improving the size and quality of natural image datasets but are not yet optimized for medical imaging.

Data Augmentation Medical Diagnosis

Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation

no code implementations29 Oct 2022 Abhishek Srivastava, Debesh Jha, Bulent Aydogan, Mohamed E. Abazeed, Ulas Bagci

Head and Neck (H\&N) organ-at-risk (OAR) and tumor segmentations are essential components of radiation therapy planning.

Tumor Segmentation

DilatedSegNet: A Deep Dilated Segmentation Network for Polyp Segmentation

1 code implementation24 Oct 2022 Nikhil Kumar Tomar, Debesh Jha, Ulas Bagci

DilatedSegNet is an encoder-decoder network that uses pre-trained ResNet50 as the encoder from which we extract four levels of feature maps.

COROID: A Crowdsourcing-based Companion Drones to Tackle Current and Future Pandemics

no code implementations19 Jul 2022 Ashish Rauniyar, Desta Haileselassie Hagos, Debesh Jha, Jan Erik Håkegård

Therefore, we believe that the COROID drone is innovative and has a huge potential to tackle COVID-19 and future pandemics.

TransResU-Net: Transformer based ResU-Net for Real-Time Colonoscopy Polyp Segmentation

1 code implementation17 Jun 2022 Nikhil Kumar Tomar, Annie Shergill, Brandon Rieders, Ulas Bagci, Debesh Jha

With high efficacy in our performance metrics, we concluded that TransResU-Net could be a strong benchmark for building a real-time polyp detection system for the early diagnosis, treatment, and prevention of colorectal cancer.

Video Capsule Endoscopy Classification using Focal Modulation Guided Convolutional Neural Network

1 code implementation16 Jun 2022 Abhishek Srivastava, Nikhil Kumar Tomar, Ulas Bagci, Debesh Jha

We compare our FocalConvNet with other SOTA on Kvasir-Capsule, a large-scale VCE dataset with 44, 228 frames with 13 classes of different anomalies.

Medical Image Classification

Automatic Polyp Segmentation with Multiple Kernel Dilated Convolution Network

2 code implementations13 Jun 2022 Nikhil Kumar Tomar, Abhishek Srivastava, Ulas Bagci, Debesh Jha

The detection and removal of precancerous polyps through colonoscopy is the primary technique for the prevention of colorectal cancer worldwide.

Transformer based Generative Adversarial Network for Liver Segmentation

1 code implementation21 May 2022 Ugur Demir, Zheyuan Zhang, Bin Wang, Matthew Antalek, Elif Keles, Debesh Jha, Amir Borhani, Daniela Ladner, Ulas Bagci

The premise behind this choice is that the self-attention mechanism of the Transformers allows the network to aggregate the high dimensional feature and provide global information modeling.

Image Segmentation Liver Segmentation +1

TGANet: Text-guided attention for improved polyp segmentation

1 code implementation9 May 2022 Nikhil Kumar Tomar, Debesh Jha, Ulas Bagci, Sharib Ali

Even though there are deep learning methods developed for this task, variability in polyp size can impact model training, thereby limiting it to the size attribute of the majority of samples in the training dataset that may provide sub-optimal results to differently sized polyps.

Medical Image Segmentation Polyp Segmentation

PAANet: Progressive Alternating Attention for Automatic Medical Image Segmentation

no code implementations20 Nov 2021 Abhishek Srivastava, Sukalpa Chanda, Debesh Jha, Michael A. Riegler, Pål Halvorsen, Dag Johansen, Umapada Pal

We develop progressive alternating attention dense (PAAD) blocks, which construct a guiding attention map (GAM) after every convolutional layer in the dense blocks using features from all scales.

Decision Making Image Segmentation +2

MedAI: Transparency in Medical Image Segmentation

1 code implementation Nordic Machine Intelligence 2021 Steven Hicks, Debesh Jha, Vajira Thambawita, Pål Halvorsen, Bjørn-Jostein Singstad, Sachin Gaur, Klas Pettersen, Morten Goodwin, Sravanthi Parasa, Thomas de Lange, Michael Riegler

MedAI: Transparency in Medical Image Segmentation is a challenge held for the first time at the Nordic AI Meet that focuses on medical image segmentation and transparency in machine learning (ML)-based systems.

Image Segmentation Medical Image Segmentation +1

A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation

1 code implementation26 Jul 2021 Debesh Jha, Pia H. Smedsrud, Dag Johansen, Thomas de Lange, Håvard D. Johansen, Pål Halvorsen, Michael A. Riegler

To explore the generalization capability of ResUNet++ on different publicly available polyp datasets, so that it could be used in a real-world setting, we performed an extensive cross-dataset evaluation.

 Ranked #1 on Medical Image Segmentation on CVC-ColonDB (using extra training data)

Medical Image Segmentation

Exploring Deep Learning Methods for Real-Time Surgical Instrument Segmentation in Laparoscopy

no code implementations5 Jul 2021 Debesh Jha, Sharib Ali, Nikhil Kumar Tomar, Michael A. Riegler, Dag Johansen, Håvard D. Johansen, Pål Halvorsen

Minimally invasive surgery is a surgical intervention used to examine the organs inside the abdomen and has been widely used due to its effectiveness over open surgery.

Medical Image Segmentation

Meta-learning with implicit gradients in a few-shot setting for medical image segmentation

no code implementations6 Jun 2021 Rabindra Khadga, Debesh Jha, Steven Hicks, Vajira Thambawita, Michael A. Riegler, Sharib Ali, Pål Halvorsen

To our knowledge, this is the first work that exploits iMAML for medical image segmentation and explores the strength of the model on scenarios such as meta-training on unique and mixed instances of lesion datasets.

Few-Shot Learning Image Segmentation +2

NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy

1 code implementation22 Apr 2021 Debesh Jha, Nikhil Kumar Tomar, Sharib Ali, Michael A. Riegler, Håvard D. Johansen, Dag Johansen, Thomas de Lange, Pål Halvorsen

To utilize automated methods in clinical settings, it is crucial to design lightweight models with low latency such that they can be integrated with low-end endoscope hardware devices.

Colorectal Polyps Characterization Instrument Recognition +3

FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation

1 code implementation31 Mar 2021 Nikhil Kumar Tomar, Debesh Jha, Michael A. Riegler, Håvard D. Johansen, Dag Johansen, Jens Rittscher, Pål Halvorsen, Sharib Ali

We propose a novel architecture called feedback attention network (FANet) that unifies the previous epoch mask with the feature map of the current training epoch.

Hard Attention Image Segmentation +2

DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation

3 code implementations8 Jun 2020 Debesh Jha, Michael A. Riegler, Dag Johansen, Pål Halvorsen, Håvard D. Johansen

The encouraging results, produced on various medical image segmentation datasets, show that DoubleU-Net can be used as a strong baseline for both medical image segmentation and cross-dataset evaluation testing to measure the generalizability of Deep Learning (DL) models.

Cell Segmentation Colorectal Polyps Characterization +5

ResUNet++: An Advanced Architecture for Medical Image Segmentation

6 code implementations16 Nov 2019 Debesh Jha, Pia H. Smedsrud, Michael A. Riegler, Dag Johansen, Thomas de Lange, Pal Halvorsen, Havard D. Johansen

Accurate computer-aided polyp detection and segmentation during colonoscopy examinations can help endoscopists resect abnormal tissue and thereby decrease chances of polyps growing into cancer.

Colorectal Polyps Characterization Image Segmentation +2

Kvasir-SEG: A Segmented Polyp Dataset

no code implementations16 Nov 2019 Debesh Jha, Pia H. Smedsrud, Michael A. Riegler, Pål Halvorsen, Thomas de Lange, Dag Johansen, Håvard D. Johansen

In this paper, we present Kvasir-SEG: an open-access dataset of gastrointestinal polyp images and corresponding segmentation masks, manually annotated by a medical doctor and then verified by an experienced gastroenterologist.

 Ranked #1 on Polyp Segmentation on Kvasir-SEG (DSC metric)

Image Segmentation Medical Image Segmentation +1

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