Search Results for author:
Found 24 papers, 15 papers with code
PolypDB: A Curated Multi-Center Dataset for Development of AI Algorithms in Colonoscopy
More information about the dataset, segmentation, detection, federated learning benchmark and train-test split can be found at \url{https://github. com/DebeshJha/PolypDB}.
MDNet: Multi-Decoder Network for Abdominal CT Organs Segmentation
Each decoder network is connected to a different part of the encoder via a multi-scale feature enhancement dilated block.
CT Liver Segmentation via PVT-based Encoding and Refined Decoding
Accurate liver segmentation from CT scans is essential for effective diagnosis and treatment planning.
Ranked #5 on
Liver Segmentation
on LiTS2017
Adaptive Smooth Activation for Improved Disease Diagnosis and Organ Segmentation from Radiology Scans
We apply this new activation function to two important and commonly used general tasks in medical image analysis: automatic disease diagnosis and organ segmentation in CT and MRI.
Prototype Learning for Out-of-Distribution Polyp Segmentation
Our model is designed to perform effectively on out-of-distribution (OOD) datasets from multiple centers.
Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges
Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps.
GastroVision: A Multi-class Endoscopy Image Dataset for Computer Aided Gastrointestinal Disease Detection
Integrating real-time artificial intelligence (AI) systems in clinical practices faces challenges such as scalability and acceptance.
TransRUPNet for Improved Polyp Segmentation
The proposed architecture, TransRUPNet, is an encoder-decoder network consisting of three encoder and decoder blocks with additional upsampling blocks at the end of the network.
A Conceptual Algorithm for Applying Ethical Principles of AI to Medical Practice
The democratization of such AI tools can reduce the cost of care, optimize resource allocation, and improve the quality of care.
TransNetR: Transformer-based Residual Network for Polyp Segmentation with Multi-Center Out-of-Distribution Testing
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.
Ranked #2 on
Polyp Segmentation
on PolypGen
RUPNet: Residual upsampling network for real-time polyp segmentation
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.
Ranked #56 on
Medical Image Segmentation
on Kvasir-SEG
DilatedSegNet: A Deep Dilated Segmentation Network for Polyp Segmentation
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
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
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.
Automatic Polyp Segmentation with Multiple Kernel Dilated Convolution Network
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
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.
Ranked #3 on
Polyp Segmentation
on Kvasir-SEG
Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge
Polyps are well-known cancer precursors identified by colonoscopy.
Exploring Deep Learning Methods for Real-Time Surgical Instrument Segmentation in Laparoscopy
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.
Ranked #1 on
Medical Image Segmentation
on ROBUST-MIS
NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy
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.
Ranked #1 on
Medical Image Segmentation
on KvasirCapsule-SEG
Colorectal Polyps Characterization
Instrument Recognition
+4
FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation
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.
Ranked #2 on
Medical Image Segmentation
on EM
Automatic Polyp Segmentation using Fully Convolutional Neural Network
Then it is tested on a separate unseen dataset to validate the efficiency and speed of the segmentation model.
Automatic Polyp Segmentation using U-Net-ResNet50
Polyps are the predecessors to colorectal cancer which is considered as one of the leading causes of cancer-related deaths worldwide.
DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation
Colonoscopy is the gold standard for examination and detection of colorectal polyps.
Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning
Benchmarking of novel methods can provide a direction to the development of automated polyp detection and segmentation tasks.
