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Found 8 papers, 2 papers with code
When Eye-Tracking Meets Machine Learning: A Systematic Review on Applications in Medical Image Analysis
Eye-gaze tracking research offers significant promise in enhancing various healthcare-related tasks, above all in medical image analysis and interpretation.
Towards Machine Learning-based Quantitative Hyperspectral Image Guidance for Brain Tumor Resection
For tissue type, at least four of the five fluorophore abundances were found to be significantly different (p < 0. 01) between all classes.
TPMIL: Trainable Prototype Enhanced Multiple Instance Learning for Whole Slide Image Classification
Due to the high resolution of the WSI and the unavailability of patch-level annotations, WSI classification is usually formulated as a weakly supervised problem, which relies on multiple instance learning (MIL) based on patches of a WSI.
Domain-Specific Pre-training Improves Confidence in Whole Slide Image Classification
To investigate the effect of domain-specific pre-training, we considered the current state-of-the-art multiple-instance learning models, 1) CLAM, an attention-based model, and 2) TransMIL, a self-attention-based model, and evaluated the models' confidence and predictive performance in detecting primary brain tumors - gliomas.
Impact of Spherical Coordinates Transformation Pre-processing in Deep Convolution Neural Networks for Brain Tumor Segmentation and Survival Prediction
Pre-processing and Data Augmentation play an important role in Deep Convolutional Neural Networks (DCNN).
Spherical coordinates transformation pre-processing in Deep Convolution Neural Networks for brain tumor segmentation in MRI
Magnetic Resonance Imaging (MRI) is used in everyday clinical practice to assess brain tumors.
Detecting Transaction-based Tax Evasion Activities on Social Media Platforms Using Multi-modal Deep Neural Networks
This paper presents a machine learning based Regtech tool for international tax authorities to detect transaction-based tax evasion activities on social media platforms.
Morphometry-Based Longitudinal Neurodegeneration Simulation with MR Imaging
We present a longitudinal MR simulation framework which simulates the future neurodegenerative progression by outputting the predicted follow-up MR image and the voxel-based morphometry (VBM) map.
