Titlebook: Medical Image Learning with Limited and Noisy Data; Second International Zhiyun Xue,Sameer Antani,Zhaohui Liang Conference proceedings 2023

美學(xué)

胎兒

Evocative

Label-Efficient Contrastive Learning-Based Model for?Nuclei Detection and?Classification in?3D Cardim Intensity Projection (MIP) to convert immunofluorescent images with multiple slices to 2D images, which can cause signals from different z-stacks to falsely appear associated with each other. To overcome this, we devised an Extended Maximum Intensity Projection (EMIP) approach that addresses issue

智力高

Affordable Graph Neural Network Framework Using Topological Graph Contractionmory-efficient GNN training framework (C-QSIGN), which incorporates our proposed contraction method along with several other state-of-the-art (SOTA) methods. Furthermore, we benchmarked our proposed model performance in terms of prediction quality and GPU usage against other SOTA methods. We show th

雜色

救護(hù)車

A Multitask Framework for?Label Refinement and?Lesion Segmentation in?Clinical Brain ImagingD scans. In extensive experiments on both proprietary and public clinical brain imaging datasets, we demonstrate that our end-to-end framework offers strong performance improvements over prevailing baselines on both label refinement and lesion segmentation. Our proposed framework maintains performan

Charitable

圓柱

Feasibility of?Universal Anomaly Detection Without Knowing the?Abnormality in?Medical Imagesnomaly detection model during the validation phase using only normal images, and (3) proposing a simple decision-level ensemble method to leverage the advantage of different kinds of anomaly detection without knowing the abnormality. The results of our experiments indicate that none of the evaluated

生存環(huán)境

rectocele

Masked Image Modeling for?Label-Efficient Segmentation in?Two-Photon Excitation Microscopy of intensity and foreground structures, and inter-channel correlations that are specific to microscopy images. We show that these methods are effective for generating representations of TPEM images, and identify novel insights on how MIM can be modified to yield more salient image representations f