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Found 18 papers, 6 papers with code
Colonoscopy 3D Video Dataset with Paired Depth from 2D-3D Registration
In this work, we present a Colonoscopy 3D Video Dataset (C3VD) acquired with a high definition clinical colonoscope and high-fidelity colon models for benchmarking computer vision methods in colonoscopy.
GAN Inversion for Data Augmentation to Improve Colonoscopy Lesion Classification
This study demonstrates that synthetic colonoscopy images generated by Generative Adversarial Network (GAN) inversion can be used as training data to improve the lesion classification performance of deep learning models.
A Deep Learning Bidirectional Temporal Tracking Algorithm for Automated Blood Cell Counting from Non-invasive Capillaroscopy Videos
Compared to manual blood cell counting, CycleTrack achieves 96. 58 $\pm$ 2. 43% cell counting accuracy among 8 test videos with 1000 frames each compared to 93. 45% and 77. 02% accuracy for independent CenterTrack and SORT almost without additional time expense.
Improving colonoscopy lesion classification using semi-supervised deep learning
While data-driven approaches excel at many image analysis tasks, the performance of these approaches is often limited by a shortage of annotated data available for training.
VR-Caps: A Virtual Environment for Capsule Endoscopy
Current capsule endoscopes and next-generation robotic capsules for diagnosis and treatment of gastrointestinal diseases are complex cyber-physical platforms that must orchestrate complex software and hardware functions.
Rapid tissue oxygenation mapping from snapshot structured-light images with adversarial deep learning
When tested on human feet, a cross-validated OxyGAN maps tissue oxygenation with an accuracy of 96. 5%.
EndoSLAM Dataset and An Unsupervised Monocular Visual Odometry and Depth Estimation Approach for Endoscopic Videos: Endo-SfMLearner
The codes and the link for the dataset are publicly available at https://github. com/CapsuleEndoscope/EndoSLAM.
EndoL2H: Deep Super-Resolution for Capsule Endoscopy
Although wireless capsule endoscopy is the preferred modality for diagnosis and assessment of small bowel diseases, the poor camera resolution is a substantial limitation for both subjective and automated diagnostics.
SLAM Endoscopy enhanced by adversarial depth prediction
Medical endoscopy remains a challenging application for simultaneous localization and mapping (SLAM) due to the sparsity of image features and size constraints that prevent direct depth-sensing.
GANPOP: Generative Adversarial Network Prediction of Optical Properties from Single Snapshot Wide-field Images
In human gastrointestinal specimens, GANPOP estimates both reduced scattering and absorption coefficients at 660 nm from a single 0. 2/mm spatial frequency illumination image with 58% higher accuracy than SSOP.
Structured Prediction using cGANs with Fusion Discriminator
We propose the fusion discriminator, a single unified framework for incorporating conditional information into a generative adversarial network (GAN) for a variety of distinct structured prediction tasks, including image synthesis, semantic segmentation, and depth estimation.
Multimodal Densenet
In this work, we propose Multimodal DenseNet, a novel architecture for fusing multimodal data.
DeepLSR: a deep learning approach for laser speckle reduction
In images of gastrointestinal tissues, DeepLSR reduces laser speckle noise by 6. 4 dB, compared to a 2. 9 dB reduction from optimized non-local means processing, a 3. 0 dB reduction from BM3D, and a 3. 7 dB reduction from an optical speckle reducer utilizing an oscillating diffuser.
Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images
However, CNNs require large amounts of labeled histopathology data.
Rethinking Monocular Depth Estimation with Adversarial Training
Most existing approaches treat depth estimation as a regression problem with a local pixel-wise loss function.
Deep Learning with Cinematic Rendering: Fine-Tuning Deep Neural Networks Using Photorealistic Medical Images
Our experiments demonstrate that: (a) Convolutional Neural Networks (CNNs) trained on synthetic data and fine-tuned on photorealistic cinematically rendered data adapt better to real medical images and demonstrate more robust performance when compared to networks with no fine-tuning, (b) these fine-tuned networks require less training data to converge to an optimal solution, and (c) fine-tuning with data from a variety of photorealistic rendering conditions of the same scene prevents the network from learning patient-specific information and aids in generalizability of the model.
Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training
We propose an alternative framework that uses a reverse flow, where adversarial training is used to make real medical images more like synthetic images, and hypothesize that clinically-relevant features can be preserved via self-regularization.
Deep Learning and Conditional Random Fields-based Depth Estimation and Topographical Reconstruction from Conventional Endoscopy
We show that the estimated depth maps can be used for reconstructing the topography of the mucosa from conventional colonoscopy images.
