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Found 16 papers, 8 papers with code
Predicting Risk of Pulmonary Fibrosis Formation in PASC Patients
We demonstrate the effectiveness of Grad-CAM visualization and radiomics-based feature analysis in providing clinically relevant insights for PASC-related lung fibrosis prediction.
Liver Cirrhosis Stage Estimation from MRI with Deep Learning
However, diagnosing cirrhosis in its early stages is challenging, and patients often present with life-threatening complications.
A Reverse Mamba Attention Network for Pathological Liver Segmentation
The architecture's generalizability is further validated on the cancerous liver segmentation from CT scans (LiTS: Liver Tumor Segmentation dataset), yielding a Dice score of 92. 9% and mIoU of 88. 99%.
Mortality Prediction of Pulmonary Embolism Patients with Deep Learning and XGBoost
Pulmonary Embolism (PE) is a serious cardiovascular condition that remains a leading cause of mortality and critical illness, underscoring the need for enhanced diagnostic strategies.
IPMN Risk Assessment under Federated Learning Paradigm
In this study, we develop a federated learning framework for multi-center IPMN classification utilizing a comprehensive pancreas MRI dataset.
Adaptive Aggregation Weights for Federated Segmentation of Pancreas MRI
Federated learning (FL) enables collaborative model training across institutions without sharing sensitive data, making it an attractive solution for medical imaging tasks.
Large Scale MRI Collection and Segmentation of Cirrhotic Liver
Liver cirrhosis represents the end stage of chronic liver disease, characterized by extensive fibrosis and nodular regeneration that significantly increases mortality risk.
A Novel Momentum-Based Deep Learning Techniques for Medical Image Classification and Segmentation
This study proposes a deep learning-based approach for segmenting various organs from CT and MRI scans and classifying diseases.
Towards Synergistic Deep Learning Models for Volumetric Cirrhotic Liver Segmentation in MRIs
Liver cirrhosis, a leading cause of global mortality, requires precise segmentation of ROIs for effective disease monitoring and treatment planning.
Optimizing Synthetic Data for Enhanced Pancreatic Tumor Segmentation
Our findings demonstrate that: (1) strategically selecting a combination of synthetic tumor sizes is crucial for optimal segmentation outcomes, and (2) generating synthetic tumors with precise boundaries significantly improves model accuracy.
Large-Scale Multi-Center CT and MRI Segmentation of Pancreas with Deep Learning
We also collected CT scans of 1, 350 patients from publicly available sources for benchmarking purposes.
MDNet: Multi-Decoder Network for Abdominal CT Organs Segmentation
Each decoder network is connected to a different part of the encoder via a multi-scale feature enhancement dilated block.
Detection of Peri-Pancreatic Edema using Deep Learning and Radiomics Techniques
Identifying peri-pancreatic edema is a pivotal indicator for identifying disease progression and prognosis, emphasizing the critical need for accurate detection and assessment in pancreatitis diagnosis and management.
CT Liver Segmentation via PVT-based Encoding and Refined Decoding
Accurate liver segmentation from CT scans is essential for effective diagnosis and treatment planning.
Ranked #5 on
Liver Segmentation
on LiTS2017
Adaptive Smooth Activation for Improved Disease Diagnosis and Organ Segmentation from Radiology Scans
We apply this new activation function to two important and commonly used general tasks in medical image analysis: automatic disease diagnosis and organ segmentation in CT and MRI.
DiffBoost: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model
We leverage recent diffusion probabilistic models to generate realistic and diverse synthetic medical image data that preserve the essential characteristics of the original medical images by incorporating edge information of objects to guide the synthesis process.
