Titlebook: Resource-Efficient Medical Image Analysis; First MICCAI Worksho Xinxing Xu,Xiaomeng Li,Huazhu Fu Conference proceedings 2022 The Editor(s)

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Classification of 4D fMRI Images Using ML, Focusing on Computational and Memory Utilization Efficie disease (AD), mild cognitive impairment (MCI) or being cognitive normal (CN). In this research, we represent Resting-State brain activity in Regions-of-Interest (ROI) by subsets of anatomical region voxels formed by segments of a whole brain bounding box Hilbert curve resulting in an average 5× few

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,Leverage Supervised and Self-supervised Pretrain Models for Pathological Survival Analysis via a Sig in pathological image analyses. Most existing studies have focused on developing more powerful pretrained models, which are increasingly unscalable for academic institutes. Very few, if any, studies have investigated how to take advantage of existing, yet heterogeneous, pretrained models for downs

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Pathological Image Contrastive Self-supervised Learning,e in many tasks in the field of computer vision. Contrastive learning methods build pre-training weight parameters by crafting positive/negative samples and optimizing their distance in the feature space. It is easy to construct positive/negative samples on natural images, but the methods cannot dir

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建筑师

,A Self-attentive Meta-learning Approach for Image-Based Few-Shot Disease Detection,er medical knowledge from common diseases to low prevalence cases using meta-learning. Indeed, compared to natural images, medical images vary less diversely from one image to another, with complex patterns and less semantic information. Hence, extracting clinically relevant features and learning a

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,Facing Annotation Redundancy: OCT Layer Segmentation with only 10 Annotated Pixels per Layer,for improved accuracy is driving the use of increasingly large dataset with fully pixel-level layer annotations. But the manual annotation process is expensive and tedious, further, the annotators also need sufficient medical knowledge which brings a great burden on the doctors. We observe that ther

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Pathological Image Contrastive Self-supervised Learning,earning on histopathological images. Results on the PatchCamelyon benchmark show that our method can improve model accuracy up to 6% while reducing the training costs, as well as reducing reliance on labeled data.