Search Results for author: Nikhil Kumar Tomar

Found 20 papers, 12 papers with code

Prototype Learning for Out-of-Distribution Polyp Segmentation

no code implementations7 Aug 2023 Nikhil Kumar Tomar, Debesh Jha, Ulas Bagci

Our model is designed to perform effectively on out-of-distribution (OOD) datasets from multiple centers.

Image Segmentation Segmentation +1

TransRUPNet for Improved Out-of-Distribution Generalization in Polyp Segmentation

no code implementations3 Jun 2023 Debesh Jha, Nikhil Kumar Tomar, Debayan Bhattacharya, Ulas Bagci

We develop a novel real-time deep learning based architecture, TransRUPNet that is based on a Transformer and residual upsampling network for colorectal polyp segmentation to improve OOD generalization.

Out-of-Distribution Generalization

TransNetR: Transformer-based Residual Network for Polyp Segmentation with Multi-Center Out-of-Distribution Testing

1 code implementation13 Mar 2023 Debesh Jha, Nikhil Kumar Tomar, Vanshali Sharma, Ulas Bagci

Therefore, we intend to develop a novel real-time deep learning based architecture, Transformer based Residual network (TransNetR), for colon polyp segmentation and evaluate its diagnostic performance.

Benchmarking Medical Image Segmentation +2

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.

Medical Image 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.


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.

Medical Image Segmentation

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.

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

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 Segmentation

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

3 code implementations22 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 +4

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

Automatic Polyp Segmentation using Fully Convolutional Neural Network

no code implementations11 Jan 2021 Nikhil Kumar Tomar

Then it is tested on a separate unseen dataset to validate the efficiency and speed of the segmentation model.


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