Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2022; 25th International C Linwei Wang,Qi Dou,Shuo Li Conference procee

Polydipsia

泛濫

phytochemicals

代替

Intra-class Contrastive Learning Improves Computer Aided Diagnosis of?Breast Cancer in?Mammographyview property of mammograms to sample contrastive image pairs. Unlike previous multi-task learning approaches, our method improves cancer detection performance without additional annotations. Experimental results further demonstrate that the proposed losses produce discriminative intra-class feature

Crater

BoxPolyp: Boost Generalized Polyp Segmentation Using Extra Coarse Bounding Box Annotationsppearance consistency of the same polyp, an image consistency (IC) loss is designed. Such IC loss explicitly narrows the distance between features extracted by two different networks, which improves the robustness of the model. Note that our BoxPolyp is a plug-and-play model, which can be merged int

賠償

FFCNet: Fourier Transform-Based Frequency Learning and?Complex Convolutional Network for?Colon Disea Moreover, the image patch scrambling module in FFCNet generates random local spectral blocks, empowering the network to learn long-range and local disease-specific features and improving the discriminative ability of hard samples. We evaluated the proposed FFCNet on an in-house dataset with 2568 co

tooth-decay

Contrastive Transformer-Based Multiple Instance Learning for?Weakly Supervised Polyp Frame Detectiono and snippet-level anomaly scores. A contrastive snippet mining method is also proposed to enable an effective modelling of the challenging polyp cases. The resulting method achieves a detection accuracy that is substantially better than current state-of-the-art approaches on a new large-scale colo

前奏曲

Lesion-Aware Dynamic Kernel for?Polyp Segmentationhe extracted lesion representation to enhance the feature contrast between the polyp and background regions by a tailored lesion-aware cross-attention module (LCA), and design an efficient self-attention module (ESA) to capture long-range context relations, further improving the segmentation accurac

discord

Stepwise Feature Fusion: Local Guides Globalrogressive Locality Decoder can be adapted to the pyramid Transformer backbone to emphasize local features and restrict attention dispersion. The SSFormer achieves state-of-the-art performance in both learning and generalization assessment.

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