A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Bayesian method transfers knowledge between brain tumor datasets.
Bayesian method transfers knowledge between brain tumor datasets for MRI segmentation.
Bayesian variational inference improves medical image segmentation confidence.
Deep learning segments spinal metastases in MR images.
Lung segmentation accuracy varies little across diverse datasets.
Enhanced deep learning model improves tumor segmentation in ultrasound images.
Paper reduces Hausdorff Distance in medical image segmentation.
Fast, accurate thalamus segmentation method for MS and ET.
A novel 3D U-Net approach improves kidney tumor segmentation in medical imaging.
Model infers mineral locations from geospatial data, improving predictions with auxiliary data.
DICE learns population dynamics from discrete samples.
In this work, we present a comparison of a shallow and a deep learning architecture for the automated segmentation of white matter lesions in MR images of multiple sclerosis patients. In particular, we train and test both methods on early stage disease patients, to verify their performance in challenging conditions, mo…
New biomarker predicts MRgFUS treatment outcome without contrast agents.
Deep learning models for semantic segmentation of images require large amounts of data. In the medical imaging domain, acquiring sufficient data is a significant challenge. Labeling medical image data requires expert knowledge. Collaboration between institutions could address this challenge, but sharing medical data to…
U-Det improves lung nodule segmentation in CT images.
AI tool automates blood segmentation from head CT scans after SAH.
DICE estimates data influence cascade in decentralized learning networks.
Few-shot brain segmentation achieved with weak labels and deep networks.
Volumetric analysis of brain ventricle (BV) structure is a key tool in the study of central nervous system development in embryonic mice. High-frequency ultrasound (HFU) is the only non-invasive, real-time modality available for rapid volumetric imaging of embryos in utero. However, manual segmentation of the BV from H…
A new framework for consistent segmentation evaluation reduces operating losses.
DiCE uses diverse agents to explore and learn, avoiding local minima.
Challenge aims to develop automated meningioma MRI segmentation models.
White matter hyperintensity (WMH) is commonly found in elder individuals and appears to be associated with brain diseases. U-net is a convolutional network that has been widely used for biomedical image segmentation. Recently, U-net has been successfully applied to WMH segmentation. Random initialization is usally used…
Unified DICE estimators as regularized Lagrangians for improved off-policy evaluation.
Optimization framework for reconstructing missing mandible segments.
Defines an implied CO2-price to cover climate change costs, finding it significantly higher than the SCC.
We consider the problem of predicting several response variables using the same set of explanatory variables. This setting naturally induces a group structure over the coefficient matrix, in which every explanatory variable corresponds to a set of related coefficients. Most of the existing methods that utilize this gro…
Kidney function evaluation using dynamic contrast-enhanced MRI (DCE-MRI) images could help in diagnosis and treatment of kidney diseases of children. Automatic segmentation of renal parenchyma is an important step in this process. In this paper, we propose a time and memory efficient fully automated segmentation method…
This work shows cosine similarity is equivalent to Pearson correlation for word vectors, but not all vectors are suitable for cosine.
Over half a million individuals are diagnosed with head and neck cancer each year worldwide. Radiotherapy is an important curative treatment for this disease, but it requires manual time consuming delineation of radio-sensitive organs at risk (OARs). This planning process can delay treatment, while also introducing int…
We present a novel approach to automatically segment magnetic resonance (MR) images of the human brain into anatomical regions. Our methodology is based on a deep artificial neural network that assigns each voxel in an MR image of the brain to its corresponding anatomical region. The inputs of the network capture infor…
Deep learning algorithms produces state-of-the-art results for different machine learning and computer vision tasks. To perform well on a given task, these algorithms require large dataset for training. However, deep learning algorithms lack generalization and suffer from over-fitting whenever trained on small dataset,…
This paper adapts PATE for semantic segmentation while maintaining privacy.
Study evaluates different saliency maps for CT image classification.
There are many studies dealing with the analysis of similarity among currencies in foreign exchange market by using network analysis approach. In those studies, each currency is represented by a univariate time series of exchange rate return. This is the standard practice to analyze the underlying information in the fo…
CAST improves spectral clustering for multi-scale data by integrating reachability similarity.
We study the dynamic interactions and structural changes in global financial indices in the years 1998-2012. We apply a principal component analysis (PCA) to cross-correlation coefficients of the stock indices. We calculate the correlations between principal components (PCs) and each asset, known as PC coefficients. A …
Deep learning models trained on adult cardiac MRI data struggle to accurately segment rare congenital heart diseases.
Background: Cardiac MRI derived biventricular mass and function parameters, such as end-systolic volume (ESV), end-diastolic volume (EDV), ejection fraction (EF), stroke volume (SV), and ventricular mass (VM) are clinically well established. Image segmentation can be challenging and time-consuming, due to the complex a…
MRI method predicts glioma features, survival, and endothelial proliferation.
We express the coefficients of the Hirzebruch L-polynomials in terms of certain alternating multiple zeta values. In particular, we show that every monomial in the Pontryagin classes appears with a non-zero coefficient, with the expected sign. Similar results hold for the polynomials associated to the A-hat genus.
RankSEG-RMA improves semantic segmentation efficiency and applicability.
Model shows how discount rates affect intergenerational equity in climate mitigation.
Probabilistic atlases provide essential spatial contextual information for image interpretation, Bayesian modeling, and algorithmic processing. Such atlases are typically constructed by grouping subjects with similar demographic information. Importantly, use of the same scanner minimizes inter-group variability. Howeve…
Brain cancer can be very fatal, but chances of survival increase through early detection and treatment. Doctors use Magnetic Resonance Imaging (MRI) to detect and locate tumors in the brain, and very carefully analyze scans to segment brain tumors. Manual segmentation is time consuming and tiring for doctors, and it ca…
Feature extraction is a very crucial task in image and pixel (voxel) classification and regression in biomedical image modeling. In this work we present a machine learning based feature extraction scheme based on inception models for pixel classification tasks. We extract features under multi-scale and multi-layer sche…
Let M,N and B\subset N be compact smooth manifolds of dimensions n+k,n and \ell, respectively. Given a map f from M to N, we give homological conditions under which g^{-1}(B) has nontrivial cohomology (with local coefficients) for any map g homotopic to f. We also show that a certain cohomology class in H^j(N,N-B) is P…
Paper presents a technique using Spearman's Rank Correlation Coefficient for KE in TDs.