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Found 13 papers, 3 papers with code
EDUE: Expert Disagreement-Guided One-Pass Uncertainty Estimation for Medical Image Segmentation
Deploying deep learning (DL) models in medical applications relies on predictive performance and other critical factors, such as conveying trustworthy predictive uncertainty.
HuLP: Human-in-the-Loop for Prognosis
This paper introduces HuLP, a Human-in-the-Loop for Prognosis model designed to enhance the reliability and interpretability of prognostic models in clinical contexts, especially when faced with the complexities of missing covariates and outcomes.
Advanced Tumor Segmentation in Medical Imaging: An Ensemble Approach for BraTS 2023 Adult Glioma and Pediatric Tumor Tasks
This study outlines our methodology for segmenting tumors in the context of two distinct tasks from the BraTS 2023 challenge: Adult Glioma and Pediatric Tumors.
Reinforcement Learning for Solving Stochastic Vehicle Routing Problem with Time Windows
This paper introduces a reinforcement learning approach to optimize the Stochastic Vehicle Routing Problem with Time Windows (SVRP), focusing on reducing travel costs in goods delivery.
Reinforcement Learning for Solving Stochastic Vehicle Routing Problem
This study addresses a gap in the utilization of Reinforcement Learning (RL) and Machine Learning (ML) techniques in solving the Stochastic Vehicle Routing Problem (SVRP) that involves the challenging task of optimizing vehicle routes under uncertain conditions.
LegoNet: Alternating Model Blocks for Medical Image Segmentation
Since the emergence of convolutional neural networks (CNNs), and later vision transformers (ViTs), the common paradigm for model development has always been using a set of identical block types with varying parameters/hyper-parameters.
Diagnosis and Prognosis of Head and Neck Cancer Patients using Artificial Intelligence
Cancer is one of the most life-threatening diseases worldwide, and head and neck (H&N) cancer is a prevalent type with hundreds of thousands of new cases recorded each year.
MGMT promoter methylation status prediction using MRI scans? An extensive experimental evaluation of deep learning models
The number of studies on deep learning for medical diagnosis is expanding, and these systems are often claimed to outperform clinicians.
TMSS: An End-to-End Transformer-based Multimodal Network for Segmentation and Survival Prediction
We propose TMSS, an end-to-end Transformer based Multimodal network for Segmentation and Survival prediction that leverages the superiority of transformers that lies in their abilities to handle different modalities.
Super Images -- A New 2D Perspective on 3D Medical Imaging Analysis
In medical imaging analysis, deep learning has shown promising results.
An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Data
The main issue when using clinical and imaging data to train a deep learning model is to decide on how to combine the information from these sources.
Object Detection in Aerial Images: What Improves the Accuracy?
In this work, we investigate the impact of Faster R-CNN for aerial object detection and explore numerous strategies to improve its performance for aerial images.
Automatic Segmentation of Head and Neck Tumor: How Powerful Transformers Are?
Cancer is one of the leading causes of death worldwide, and head and neck (H&N) cancer is amongst the most prevalent types.
