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Found 9 papers, 0 papers with code
Benchmarking the Cell Image Segmentation Models Robustness under the Microscope Optical Aberrations
Overall, this research aims to guide researchers in effectively utilizing cell segmentation models in the presence of minor optical aberrations.
Harnessing Intra-group Variations Via a Population-Level Context for Pathology Detection
Realizing sufficient separability between the distributions of healthy and pathological samples is a critical obstacle for pathology detection convolutional models.
The neural network models with delays for solving absolute value equations
Compared with the existing neural network models for solving the AVE, the proposed models feature the ability of solving a class of AVE with $\|A^{-1}\|>1$.
Ammonia-Net: A Multi-task Joint Learning Model for Multi-class Segmentation and Classification in Tooth-marked Tongue Diagnosis
After a number of extensive experiments, the experimental results show that the proposed model achieves 99. 06% accuracy in the two-class classification task of tooth-marked tongue identification and 80. 02%.
GAME: Generalized deep learning model towards multimodal data integration for early screening of adolescent mental disorders
The timely identification of mental disorders in adolescents is a global public health challenge. Single factor is difficult to detect the abnormality due to its complex and subtle nature.
Object Detection for Caries or Pit and Fissure Sealing Requirement in Children's First Permanent Molars
The best result for YOLOXs model with tiling strategy is 72. 3 mAP. 5, while the best result without tiling strategy is 71. 2.
Neuro-Symbolic Learning: Principles and Applications in Ophthalmology
Neural networks have been rapidly expanding in recent years, with novel strategies and applications.
Mixed-UNet: Refined Class Activation Mapping for Weakly-Supervised Semantic Segmentation with Multi-scale Inference
Deep learning techniques have shown great potential in medical image processing, particularly through accurate and reliable image segmentation on magnetic resonance imaging (MRI) scans or computed tomography (CT) scans, which allow the localization and diagnosis of lesions.
RCMNet: A deep learning model assists CAR-T therapy for leukemia
Although testing on the CAR-T cells dataset, a decent performance is observed, which is attributed to the limited size of the dataset.
