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Found 76 papers, 44 papers with code
PRS-Med: Position Reasoning Segmentation with Vision-Language Model in Medical Imaging
Recent advancements in prompt-based medical image segmentation have enabled clinicians to identify tumors using simple input like bounding boxes or text prompts.
FocusNet: Transformer-enhanced Polyp Segmentation with Local and Pooling Attention
Colonoscopy is vital in the early diagnosis of colorectal polyps.
A Reverse Mamba Attention Network for Pathological Liver Segmentation
The architecture's generalizability is further validated on the cancerous liver segmentation from CT scans (LiTS: Liver Tumor Segmentation dataset), yielding a Dice score of 92. 9% and mIoU of 88. 99%.
Liver Cirrhosis Stage Estimation from MRI with Deep Learning
However, diagnosing cirrhosis in its early stages is challenging, and patients often present with life-threatening complications.
Diverse Image Generation with Diffusion Models and Cross Class Label Learning for Polyp Classification
For precise classification and early diagnosis of such polyps, the medical procedure of colonoscopy has been widely adopted paired with various imaging techniques, including narrow band imaging and white light imaging.
SAM-Mamba: Mamba Guided SAM Architecture for Generalized Zero-Shot Polyp Segmentation
Polyp segmentation in colonoscopy is crucial for detecting colorectal cancer.
Large Scale MRI Collection and Segmentation of Cirrhotic Liver
Liver cirrhosis represents the end stage of chronic liver disease, characterized by extensive fibrosis and nodular regeneration that significantly increases mortality risk.
Frequency-Based Federated Domain Generalization for Polyp Segmentation
Federated Learning (FL) offers a powerful strategy for training machine learning models across decentralized datasets while maintaining data privacy, yet domain shifts among clients can degrade performance, particularly in medical imaging tasks like polyp segmentation.
Classification of Endoscopy and Video Capsule Images using CNN-Transformer Model
The results underscore the potential of our hybrid CNN-Transformer model in aiding the early and accurate detection of gastrointestinal (GI) anomalies.
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}.
A Novel Momentum-Based Deep Learning Techniques for Medical Image Classification and Segmentation
This study proposes a deep learning-based approach for segmenting various organs from CT and MRI scans and classifying diseases.
Towards Synergistic Deep Learning Models for Volumetric Cirrhotic Liver Segmentation in MRIs
Liver cirrhosis, a leading cause of global mortality, requires precise segmentation of ROIs for effective disease monitoring and treatment planning.
SAM-EG: Segment Anything Model with Egde Guidance framework for efficient Polyp Segmentation
Polyp segmentation, a critical concern in medical imaging, has prompted numerous proposed methods aimed at enhancing the quality of segmented masks.
Ranked #14 on
Medical Image Segmentation
on ETIS-LARIBPOLYPDB
PP-SAM: Perturbed Prompts for Robust Adaptation of Segment Anything Model for Polyp Segmentation
To this end, we utilize variable perturbed bounding box prompts (BBP) to enrich the learning context and enhance the model's robustness to BBP perturbations during inference.
Large-Scale Multi-Center CT and MRI Segmentation of Pancreas with Deep Learning
We also collected CT scans of 1, 350 patients from publicly available sources for benchmarking purposes.
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.
PAM-UNet: Shifting Attention on Region of Interest in Medical Images
Computer-aided segmentation methods can assist medical personnel in improving diagnostic outcomes.
Detection of Peri-Pancreatic Edema using Deep Learning and Radiomics Techniques
Identifying peri-pancreatic edema is a pivotal indicator for identifying disease progression and prognosis, emphasizing the critical need for accurate detection and assessment in pancreatitis diagnosis and management.
Ethical Framework for Responsible Foundational Models in Medical Imaging
Foundational models (FMs) have tremendous potential to revolutionize medical imaging.
Explainable Transformer Prototypes for Medical Diagnoses
Deployments of artificial intelligence in medical diagnostics mandate not just accuracy and efficacy but also trust, emphasizing the need for explainability in machine decisions.
Harmonized Spatial and Spectral Learning for Robust and Generalized Medical Image Segmentation
This paper presents a novel approach that synergies spatial and spectral representations to enhance domain-generalized medical image segmentation.
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
PGDS: Pose-Guidance Deep Supervision for Mitigating Clothes-Changing in Person Re-Identification
Person Re-Identification (Re-ID) task seeks to enhance the tracking of multiple individuals by surveillance cameras.
Clothes Changing Person Re-Identification
Person Retrieval
+3
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.
Rethinking Intermediate Layers design in Knowledge Distillation for Kidney and Liver Tumor Segmentation
HLFD strategically distills knowledge from a combination of middle layers to earlier layers and transfers final layer knowledge to intermediate layers at both the feature and pixel levels.
SynergyNet: Bridging the Gap between Discrete and Continuous Representations for Precise Medical Image Segmentation
When evaluating skin lesion and brain tumor segmentation datasets, we observe a remarkable improvement of 1. 71% in Intersection-over Union scores for skin lesion segmentation and of 8. 58% for brain tumor segmentation.
DiffBoost: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model
We leverage recent diffusion probabilistic models to generate realistic and diverse synthetic medical image data that preserve the essential characteristics of the original medical images by incorporating edge information of objects to guide the synthesis process.
Domain Generalization with Fourier Transform and Soft Thresholding
However, it neglects background interference in the amplitude spectrum.
DSFNet: Dual-GCN and Location-fused Self-attention with Weighted Fast Normalized Fusion for Polyps Segmentation
Polyps segmentation poses a significant challenge in medical imaging due to the flat surface of polyps and their texture similarity to surrounding tissues.
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.
Self-Supervised Learning for Organs At Risk and Tumor Segmentation with Uncertainty Quantification
In this study, our goal is to show the impact of self-supervised pre-training of transformers for organ at risk (OAR) and tumor segmentation as compared to costly fully-supervised learning.
Vision Transformer for Efficient Chest X-ray and Gastrointestinal Image Classification
Convolutional neural networks (CNNs) have become the de-facto standard in medical image analysis tasks because of their ability to learn complex features from the available datasets, which makes them surpass humans in many image-understanding tasks.
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.
Domain Generalization with Adversarial Intensity Attack for Medical Image Segmentation
In real-world scenarios, however, it is common for models to encounter data from new and different domains to which they were not exposed to during training.
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 #58 on
Medical Image Segmentation
on Kvasir-SEG
Domain Generalization with Correlated Style Uncertainty
In this regard, style augmentation is a strong DG method taking advantage of instance-specific feature statistics containing informative style characteristics to synthetic novel domains.
A Critical Appraisal of Data Augmentation Methods for Imaging-Based Medical Diagnosis Applications
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.
Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation
Head and Neck (H\&N) organ-at-risk (OAR) and tumor segmentations are essential components of radiation therapy planning.
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.
An Efficient Multi-Scale Fusion Network for 3D Organ at Risk (OAR) Segmentation
Accurate segmentation of organs-at-risks (OARs) is a precursor for optimizing radiation therapy planning.
Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions
To this end, in this paper, we explore the present and future of FL technology in medical applications where data sharing is a significant challenge.
COROID: A Crowdsourcing-based Companion Drones to Tackle Current and Future Pandemics
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
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.
Transformer based Generative Adversarial Network for Liver Segmentation
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.
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.
GMSRF-Net: An improved generalizability with global multi-scale residual fusion network for polyp segmentation
The repeated fusion operations gated by CMSA and MSFS demonstrate improved generalizability of the network.
Ranked #20 on
Medical Image Segmentation
on Kvasir-SEG
PAANet: Progressive Alternating Attention for Automatic Medical Image Segmentation
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.
MedAI: Transparency in Medical Image Segmentation
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.
2020 CATARACTS Semantic Segmentation Challenge
In 2020, we released pixel-wise semantic annotations for anatomy and instruments for 4670 images sampled from 25 videos of the CATARACTS training set.
A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation
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-VideoClinicDB
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
A multi-centre polyp detection and segmentation dataset for generalisability assessment
To our knowledge, this is the most comprehensive detection and pixel-level segmentation dataset (referred to as \textit{PolypGen}) curated by a team of computational scientists and expert gastroenterologists.
Meta-learning with implicit gradients in a few-shot setting for medical image segmentation
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.
Progressively Normalized Self-Attention Network for Video Polyp Segmentation
Existing video polyp segmentation (VPS) models typically employ convolutional neural networks (CNNs) to extract features.
Ranked #10 on
Video Polyp Segmentation
on SUN-SEG-Easy (Unseen)
MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation
The proposed MSRF-Net allows to capture object variabilities and provides improved results on different biomedical datasets.
Ranked #6 on
Medical Image Segmentation
on 2018 Data Science Bowl
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
LightLayers: Parameter Efficient Dense and Convolutional Layers for Image Classification
Deep Neural Networks (DNNs) have become the de-facto standard in computer vision, as well as in many other pattern recognition tasks.
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.
Medico Multimedia Task at MediaEval 2020: Automatic Polyp Segmentation
Colorectal cancer is the third most common cause of cancer 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.
Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy
Additionally, we provide a baseline for the segmentation of the GI tools to promote research and algorithm development.
Ranked #2 on
Medical Image Segmentation
on Kvasir-Instrument
DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation
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.
An Extensive Study on Cross-Dataset Bias and Evaluation Metrics Interpretation for Machine Learning applied to Gastrointestinal Tract Abnormality Classification
A clear understanding of evaluation metrics and machine learning models with cross datasets is crucial to bring research in the field to a new quality level.
Robust Medical Instrument Segmentation Challenge 2019
The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data.
ResUNet++: An Advanced Architecture for Medical Image Segmentation
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.
Kvasir-SEG: A Segmented Polyp Dataset
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 #7 on
Polyp Segmentation
on Kvasir-SEG
The Medico-Task 2018: Disease Detection in the Gastrointestinal Tract using Global Features and Deep Learning
In this paper, we present our approach for the 2018 Medico Task classifying diseases in the gastrointestinal tract.
