849 research outputs found
The Role of Endoscopic Ultrasound in M-Staging of Gastrointestinal and Pancreaticobiliary Cancer
AbstractEndoscopic ultrasound (EUS) is an inevitable tool for locoregional staging of upper gastrointestinal, rectal, and pancreaticobiliary cancer. Transabdominal ultrasound (TUS) and computed tomography (CT) are the most important methods used for the detection of liver metastases and other distant metastases. However, despite its limited operation range, EUS and EUS-guided fine-needle biopsy (EUS-FNB) may add value to TUS and CT by detecting and proving âoccultâ liver metastases and malignant ascites as well as nonregional lymph node metastases, adrenal metastases, and pleural carcinosis in approximately 5â20% of cases of pancreaticobiliary and upper gastrointestinal tract cancer. This article is part of an expert video encyclopedia
Uncertainty modeling and interpretability in convolutional neural networks for polyp segmentation
Convolutional Neural Networks (CNNs) are propelling advances in a range of different computer vision tasks such as object detection and object segmentation. Their success has motivated research in applications of such models for medical image analysis. If CNN-based models are to be helpful in a medical context, they need to be precise, interpretable, and uncertainty in predictions must be well understood. In this paper, we develop and evaluate recent advances in uncertainty estimation and model interpretability in the context of semantic segmentation of polyps from colonoscopy images. We evaluate and enhance several architectures of Fully Convolutional Networks (FCNs) for semantic segmentation of colorectal polyps and provide a comparison between these models. Our highest performing model achieves a 76.06% mean IOU accuracy on the EndoScene dataset, a considerable improvement over the previous state-of-the-art
Density ridge manifold traversal
The density ridge framework for estimating principal curves and surfaces has in a number of recent works been shown to capture manifold structure in data in an intuitive and effective manner. However, to date there exists no efficient way to traverse these manifolds as defined by density ridges. This is unfortunate, as manifold traversal is an important problem for example for shape estimation in medical imaging, or in general for being able to characterize and understand state transitions or local variability over the data manifold. In this paper, we remedy this situation by introducing a novel manifold traversal algorithm based on geodesics within the density ridge approach. The traversal is executed in a subspace capturing the intrinsic dimensionality of the data using dimensionality reduction techniques such as principal component analysis or kernel entropy component analysis. A mapping back to the ambient space is obtained by training a neural network. We compare against maximum mean discrepancy traversal, a recent approach, and obtain promising results
Temporal overdrive recurrent neural network
In this work we present a novel recurrent neural network architecture designed to model systems characterized by multiple characteristic timescales in their dynamics. The proposed network is composed by several recurrent groups of neurons that are trained to separately adapt to each timescale, in order to improve the system identification process. We test our framework on time series prediction tasks and we show some promising, preliminary results achieved on synthetic data. To evaluate the capabilities of our network, we compare the performance with several state-of-the-art recurrent architectures
The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision Making
The Cauchy-Schwarz (CS) divergence was developed by Pr\'{i}ncipe et al. in
2000. In this paper, we extend the classic CS divergence to quantify the
closeness between two conditional distributions and show that the developed
conditional CS divergence can be simply estimated by a kernel density estimator
from given samples. We illustrate the advantages (e.g., the rigorous
faithfulness guarantee, the lower computational complexity, the higher
statistical power, and the much more flexibility in a wide range of
applications) of our conditional CS divergence over previous proposals, such as
the conditional KL divergence and the conditional maximum mean discrepancy. We
also demonstrate the compelling performance of conditional CS divergence in two
machine learning tasks related to time series data and sequential inference,
namely the time series clustering and the uncertainty-guided exploration for
sequential decision making.Comment: 23 pages, 7 figure
View it like a radiologist: Shifted windows for deep learning augmentation of CT images
Deep learning has the potential to revolutionize medical practice by
automating and performing important tasks like detecting and delineating the
size and locations of cancers in medical images. However, most deep learning
models rely on augmentation techniques that treat medical images as natural
images. For contrast-enhanced Computed Tomography (CT) images in particular,
the signals producing the voxel intensities have physical meaning, which is
lost during preprocessing and augmentation when treating such images as natural
images. To address this, we propose a novel preprocessing and intensity
augmentation scheme inspired by how radiologists leverage multiple viewing
windows when evaluating CT images. Our proposed method, window shifting,
randomly places the viewing windows around the region of interest during
training. This approach improves liver lesion segmentation performance and
robustness on images with poorly timed contrast agent. Our method outperforms
classical intensity augmentations as well as the intensity augmentation
pipeline of the popular nn-UNet on multiple datasets.Comment: 6 pages, 3 figures, accepted to MLSP 202
- âŚ