Prognosis
229 papers with code • 0 benchmarks • 0 datasets
Predict the remaining useful life
Benchmarks
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Most implemented papers
Hierarchical Graph Representations in Digital Pathology
We propose a novel multi-level hierarchical entity-graph representation of tissue specimens to model hierarchical compositions that encode histological entities as well as their intra- and inter-entity level interactions.
AeroPath: An airway segmentation benchmark dataset with challenging pathology
puzzled-hui/atm-22-related-work
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In this study, we introduce a new public benchmark dataset (AeroPath), consisting of 27 CT images from patients with pathologies ranging from emphysema to large tumors, with corresponding trachea and bronchi annotations.
FALFormer: Feature-aware Landmarks self-attention for Whole-slide Image Classification
quiil/falformer
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Herein, we propose an efficient and effective slide-level classification model, named as FALFormer, that can process a WSI as a whole so as to fully exploit the relationship among the entire patches and to improve the classification performance.
Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural Networks
WenjieDu/PyPOTS
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Existing methods address this estimation problem by interpolating within data streams or imputing across data streams (both of which ignore important information) or ignoring the temporal aspect of the data and imposing strong assumptions about the nature of the data-generating process and/or the pattern of missing data (both of which are especially problematic for medical data).
D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation
SZUHvern/D-UNet
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This function adds a weighted focal coefficient and combines two traditional loss functions.
Vox2Vox: 3D-GAN for Brain Tumour Segmentation
mdciri/Vox2Vox
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Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histological sub-regions, i. e., peritumoral edema, necrotic core, enhancing and non-enhancing tumour core.
Cutting-edge 3D Medical Image Segmentation Methods in 2020: Are Happy Families All Alike?
MIC-DKFZ/nnunet
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Segmentation is one of the most important and popular tasks in medical image analysis, which plays a critical role in disease diagnosis, surgical planning, and prognosis evaluation.
COVIDx-US -- An open-access benchmark dataset of ultrasound imaging data for AI-driven COVID-19 analytics
The COVID-19 pandemic continues to have a devastating effect on the health and well-being of the global population.
SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalities
rezazad68/smunet
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Gliomas are one of the most prevalent types of primary brain tumours, accounting for more than 30\% of all cases and they develop from the glial stem or progenitor cells.
Preoperative brain tumor imaging: models and software for segmentation and standardized reporting
Tumor segmentation models were trained using the AGU-Net architecture with different preprocessing steps and protocols.
