DeepPhase uses deep learning to recognize surgical phases in cataract surgery videos.
problem Automating surgical workflow analysis for better standardization and post-surgical assessment.
method Deep learning for instrument detection and phase classification in cataract surgery videos.
result DeepPhase models achieve 99% accuracy in instrument detection and 78% in phase recognition.
Paper proposes an active learning method for surgical workflow recognition using long-range temporal dependency.
problem Challenges in automatic surgical workflow recognition due to lack of large-scale labelled datasets.
method NL-RCNet with non-local block for capturing long-range temporal dependency and intra-clip dependency score for selection.
result Our approach outperforms state-of-the-art methods by selecting only 50% of samples for training.
Method detects surgical deviations during laparoscopic rectopexy.
problem Detecting deviations from standard surgical processes during laparoscopic rectopexy.
method Multi-dimensional non-linear temporal scaling with a hidden semi-Markov model.
result Over 90% accuracy in detecting deviations.
A deep learning method automates tensioning for surgical tissue cutting.
problem Automating tensioning for surgical tissue cutting to improve accuracy and reliability.
method Deep reinforcement learning for modeling an autonomous tensioning planner.
result The proposed method outperforms existing methods in terms of performance and robustness.
The paper proposes a method to align surgical videos using kinematic data.
problem Difficulty in learning from comparing novice to expert surgical videos due to variability in gesture duration and execution.
method A novel technique using Dynamic Time Warping to synchronize videos of the same gesture at different speeds.
result The proposed approach allows for the alignment of surgical videos, enabling better learning for novice trainees.
Convolutional neural networks improve surgical skill evaluation.
problem Manual feedback from senior surgeons is laborious, expensive, and subjective.
method Fully convolutional neural networks (CNNs) for classifying and regressing surgical skills.
result Deep neural networks achieve competitive performance on JIGSAWS dataset.
The paper uses facial keypoints to estimate post-surgical pain intensity.
problem Accurately assessing pain levels from self-reported ratings is challenging.
method The approach analyzes 2D and 3D facial keypoints to estimate pain intensity.
result The pain estimation model uses multiple instance learning.
New approach uses synthetic labels to train models on scarce annotated data for surgical phase recognition.
problem Learning surgical phase recognition from limited annotated data.
method Teacher/Student approach with a CNN-biLSTM-CRF teacher generating synthetic labels for a CNN-LSTM student.
result Improved surgical phase recognition performance with fewer annotated videos.
PHASE predicts surgical complications from physiological signals.
problem Predicting adverse surgical outcomes from physiological signals.
method Self-supervised transfer learning for physiological signals.
result PHASE outperforms other approaches in predicting five surgical complications.
Algorithm detects surgical site infections from hospital data.
problem Reduces incidence of surgical site infections in hospitals.
method Machine learning algorithms trained on hospital data.
result Identifies all SSIs with minimal false positives.
The study predicts surgical complications in Crohn's disease patients using machine learning.
problem Predicting surgical complications in Crohn's disease patients.
method Developed a novel algorithm using ensemble machine learning on 29 baseline covariates.
result Proposed pseudo-observation based estimators for evaluating predictive performance.
We aim to create a framework for transfer learning using latent factor models to learn the dependence structure between a larger source dataset and a target dataset. The methodology is motivated by our goal of building a risk-assessment model for surgery patients, using both institutional and national surgical outcomes…
Evaluating surgeon skill has predominantly been a subjective task. Development of objective methods for surgical skill assessment are of increased interest. Recently, with technological advances such as robotic-assisted minimally invasive surgery (RMIS), new opportunities for objective and automated assessment framewor…
Paper introduces OTR for efficient offline RL in surgical robotics.
problem Lack of annotated datasets for offline RL in surgical robotics.
method OTR algorithm using Optimal Transport to assign rewards to unlabeled trajectories.
result OTR enables efficient policy learning from large datasets without handcrafted rewards.
Paper presents a new validation method for simulation workflow.
problem Validation of simulation workflow is challenging and domain-dependent.
method Empirical learning-based validation procedure using AHP and machine learning.
result Validation procedure is semi-automated and efficient.
The paper improves SVM for predicting hospital readmissions by clustering patients and personalizing recommendations.
problem Predicting and preventing hospital readmissions.
method Cluster-dependent SVM with personalized prescriptions.
result Personalized recommendations reduce hospital readmissions.
We give a criterion under which a solution g(t) of the Kahler-Ricci flow contracts exceptional divisors on a compact manifold and can be uniquely continued on a new manifold. As t tends to the singular time T from each direction, we prove convergence of g(t) in the sense of Gromov-Hausdorff and smooth convergence away …
Optimization framework for reconstructing missing mandible segments.
problem Missing mandible geometry in jaw reconstructive surgeries.
method Conditional variational autoencoder (CVAE) with weighted multi-target probabilistic solution.
result Statistically significant performance improvement over CVAE.
Paper presents a workflow for reliable unsupervised learning in science.
problem Lack of standardization in unsupervised learning workflows for reproducible scientific discoveries.
method Structured workflow including data preparation, modeling, validation, and communication.
result Illustrates the importance of validation in unsupervised learning.
Despite spectacular advances in defining invariants for simply connected smooth and symplectic 4-dimensional manifolds and the discovery of effective surgical techniques, we still have been unable to classify simply connected smooth manifolds up to diffeomorphism. In these notes, adapted from six lectures given at the …
This paper analyzes machine learning workflows in climate modeling.
problem Challenges in integrating machine learning with climate modeling.
method Analysis of case studies focusing on design patterns and workflow structure.
result Synthesis of workflow design patterns across diverse projects in ML-enabled climate modeling.
The paper addresses uncalibrated uncertainty estimates for object localization.
problem Uncalibrated uncertainty estimates for object localization in safety-critical applications.
method Adapting a technique for calibrating regression models to object localization.
result Calibrated model provides more reliable uncertainty estimates.
A large fraction of the electronic health records consists of clinical measurements collected over time, such as blood tests, which provide important information about the health status of a patient. These sequences of clinical measurements are naturally represented as time series, characterized by multiple variables a…
Generative AI agents improve ERP systems by automating complex financial tasks.
problem Static, rule-based workflows limit adaptability and intelligence in ERP systems.
method Introducing Generative Business Process AI Agents (GBPAs) that integrate generative AI with business process modeling and multi-agent orchestration.
result GBPAs achieve up to 40% reduction in processing time and 94% drop in error rate.
Adaptive workflow combines fast amortized inference with MCMC for many datasets.
problem Trade-off between computational speed and sampling accuracy in Bayesian inference.
method Adaptive workflow integrating amortized inference and MCMC with principled diagnostics.
result Efficiency gains with high posterior quality on tens of thousands of datasets.
The study uses transfer learning to compare surgical outcomes across racial/ethnic subgroups.
problem Difficulty in comparing surgical outcomes due to racial/ethnic and geographic differences.
method Causal inference framework and transfer learning to incorporate data from multiple populations.
result Racial and ethnic differences in surgical outcomes are found, with non-Hispanic Black patients experiencing wide variability.
Researchers found that avoiding synthetic data generation prevents model collapse in machine learning.
problem Model collapse in machine learning where models degenerate over generations.
method Comparing discard and augment workflows, focusing on Linear Regression.
result Theoretical evidence shows that for Linear Regression, test risk is bounded by π²/6 of original data alone.
Develops workflow for synthetic insurance datasets.
problem Lack of realistic publicly available insurance datasets.
method Uses CTGAN neural network architecture to generate tabular data.
result Synthesized datasets evaluated positively in multiple aspects.
Audit financial machine learning workflows to detect spurious predictability.
problem Spurious predictability in financial machine learning models.
method Falsification audit testing predictive workflows against synthetic environments.
result Many apparent financial predictions are artifacts, not genuine.
HyperStream processes streaming data with workflow creation.
problem Processing large-scale, real-time data challenges.
method Python-based workflow engine for flexible, robust data processing.
result Overcomes limitations of other computational engines.
Helix speeds up iterative ML development by optimizing workflow execution.
problem Inefficient manual tuning of ML workflows.
method Declarative system that optimizes workflow execution end-to-end and across iterations, minimizing runtime per iteration.
result Up to an order of magnitude reduction in cumulative run time compared to state-of-the-art tools.
A new method for releasing AI workflows to avoid premature incorrect results.
problem Statistical challenges in releasing AI workflows with adaptive scoring.
method Wrapper that calibrates and accumulates evidence from high-scoring failures.
result Reduces premature incorrect release while still releasing on moderate evidence.
Machine learning workflow development is anecdotally regarded to be an iterative process of trial-and-error with humans-in-the-loop. However, we are not aware of quantitative evidence corroborating this popular belief. A quantitative characterization of iteration can serve as a benchmark for machine learning workflow d…
It is well-known that any pair of closed orientable 3-manifolds are related by a finite sequence of Dehn surgeries on knots. Furthermore Kawauchi showed that such knots can be taken to be hyperbolic. In this article, we consider the minimal length of such sequences connecting a pair of 3-manifolds, in particular, a pai…
Human-in-the-loop data analysis applications necessitate greater transparency in machine learning models for experts to understand and trust their decisions. To this end, we propose a visual analytics workflow to help data scientists and domain experts explore, diagnose, and understand the decisions made by a binary cl…
Deep learning model improves seismic rock property estimation.
problem Estimating reservoir rock properties from seismic reflection data.
method Proposes a deep learning-based seismic inversion workflow that models seismic traces spatiotemporally.
result Achieves best performance on SEAM dataset with r2 coefficient of 79.77\% Deep learning speeds up pressure prediction in carbon storage reservoirs.
problem Accurately forecasting reservoir pressure in geologic carbon storage projects with sparse well data.
method Combining InSAR surface displacement data with deep learning and data assimilation techniques.
result Workflow can predict reservoir pressure with high efficiency and uncertainty quantification.
Bayesian models can be tricked into believing false data.
problem Vulnerability of Bayesian inference to data poisoning attacks.
method Developed attacks to manipulate Bayesian posterior through deletion and replication of data.
result Demonstrated that Bayesian inference can be steered to target distributions.
Testing (conditional) independence of multivariate random variables is a task central to statistical inference and modelling in general - though unfortunately one for which to date there does not exist a practicable workflow. State-of-art workflows suffer from the need for heuristic or subjective manual choices, high c…
This thesis builds a real-time VaR calculation workflow for crypto derivatives.
problem Managing risk in volatile cryptocurrency markets.
method Applied EMWA, GARCH, and HAR models to forecast volatility; used delta-gamma-theta approach and Cornish-Fisher expansion.
result Real-time VaR estimates with millisecond calculation latencies.
Benchpress streamlines benchmarking structure learning algorithms for probabilistic models.
problem Benchmarking structure learning algorithms for probabilistic graphical models.
method Snakemake workflow for scalable, reproducible benchmarks.
result Demonstrates applicability in five typical data scenarios.
Study uses non-invasive sensors to assess microbial contamination in green salads.
problem Assessing microbial contamination in ready-to-eat green salads.
method Unified spectra analysis workflow with data normalization, feature selection, and partial least squares regression.
result Unified workflow provides efficient estimation of microbial contamination and shelf life.
We prove that a general complex Monge-Ampère flow on a Hermitian manifold can be run from an arbitrary initial condition with zero Lelong number at all points. Using this property, we confirm a conjecture of Tosatti-Weinkove: the Chern-Ricci flow performs a canonical surgical contraction. Finally, we study a generaliza…
Automates feature extraction for IMU-based activity recognition.
problem Manual feature engineering is time-consuming and limits IMU-based applications.
method FRESH algorithm for automated feature extraction.
result Workflow automates feature extraction from synchronized IMU sensors.
GSR optimizes tasks in scientific workflows, improving performance across diverse applications.
problem Uncertainty in task selection and evaluation in scientific workflow optimization.
method Generate-Select-Refine (GSR) framework that alternates between task generation and optimization.
result GSR outperforms existing LLM-based optimizers in various scientific applications.
The paper proposes a ML workflow for B2B sales prediction.
problem Predicting business to business sales outcomes using subjective human evaluations.
method A two-pipeline ML approach on Azure ML, including data enrichment and model training.
result ML predictions improve accuracy and increase sales value.
Study evaluates deep learning methods for dermatology, finding they perform poorly under non-ideal conditions.
problem Lack of robustness of deep learning methods in dermatology under real-world conditions.
method Simulated non-ideal conditions on user-submitted dermatology images.
result Deep learning methods show significant drop in accuracy and prediction changes under non-ideal conditions.
Z-Net improves 3D CT volume segmentation for surgical planning.
problem Discontinuities and class-imbalances in 3D CT volume segmentation.
method Z-Net uses anisotropic spatial separable convolutions to preserve full field-of-view.
result Z-Net achieves up to 12.6% improvement in IoU for CT segmentation.