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Found 10 papers, 3 papers with code
Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021
This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels.
Multi-modal volumetric concept activation to explain detection and classification of metastatic prostate cancer on PSMA-PET/CT
Multi-modal volumetric concept activation was used to provide global and local explanations.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
Leveraging Clinical Characteristics for Improved Deep Learning-Based Kidney Tumor Segmentation on CT
The baseline 3D U-Net showed a segmentation performance of 0. 90 for kidney and kidney masses, i. e., kidney, tumor, and cyst, 0. 29 for kidney masses, and 0. 28 for kidney tumor, while the 3D U-Net trained with cognizant sampling enhanced the segmentation performance and reached Dice scores of 0. 90, 0. 39, and 0. 38 respectively.
MixLacune: Segmentation of lacunes of presumed vascular origin
Lacunes of presumed vascular origin are fluid-filled cavities of between 3 - 15 mm in diameter, visible on T1 and FLAIR brain MRI.
MixMicrobleed: Multi-stage detection and segmentation of cerebral microbleeds
Cerebral microbleeds are small, dark, round lesions that can be visualised on T2*-weighted MRI or other sequences sensitive to susceptibility effects.
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis
With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis.
Interpretable deep learning regression for breast density estimation on MRI
In this study, we propose a method to directly assess breast density on MRI, and provide interpretations of these assessments.
Response monitoring of breast cancer on DCE-MRI using convolutional neural network-generated seed points and constrained volume growing
A constrained volume growing method uses these manually placed seed points as input and generates a tumor segmentation.
BPE and computer-extracted parenchymal enhancement for breast cancer risk, response monitoring, and prognosis
Both BPE and computer-extracted parenchymal enhancement properties have been linked to screening and diagnosis, hormone status and age, risk of development of breast cancer, response monitoring, and prognosis.
Deep Learning for Multi-Task Medical Image Segmentation in Multiple Modalities
The CNN therefore learns to identify the imaging modality, the visualised anatomical structures, and the tissue classes.
