Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021; 24th International C Marleen de Bruijne,Philippe C. Cattin,Caroli

Nutrient

時(shí)間等

Enhanced Breast Lesion Classification via Knowledge Guided Cross-Modal and Semantic Data Augmentatio complementary counterpart. Although an automated breast lesion classification system is desired, training of such a system is constrained by data scarcity and modality imbalance problems due to the lack of SWE devices in rural hospitals. To enhance the diagnosis with only US available, in this work

換話題

Multiple Meta-model Quantifying for Medical Visual Question Answeringtask. However, most of the existing medical VQA methods rely on external data for transfer learning, while the meta-data within the dataset is not fully utilized. In this paper, we present a new multiple meta-model quantifying method that effectively learns meta-annotation and leverages meaningful f

Plaque

mfTrans-Net: Quantitative Measurement of Hepatocellular Carcinoma via Multi-Function Transformer Regsses for HCC treatment and prognosis. However, direct automated quantitative measurement using the CNN-based network a still challenging task due to: (1) The lack of ability for capturing long-range dependencies of multi-anatomy in the whole medical image; (2) The lack of mechanism for fusing and se

自愛

CRP743

A Coherent Cooperative Learning Framework Based on Transfer Learning for Unsupervised Cross-Domain C labels of target domain, domain adaptation for unsupervised cross-domain classification attracts widespread attention. However, current methods take knowledge transfer model and classification model as two separate training stages, which inadequately considers and utilizes the intrinsic information

epidermis

flutter

A Segmentation-Assisted Model for Universal Lesion Detection with Partial Labelsnd timely treatment. Recently, deep neural networks have been applied for the ULD task, and existing methods assume that all the training samples are well-annotated. However, the partial label problem is unavoidable when curating large-scale datasets, where only a part of instances are annotated in

Cuisine

Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation inhy) samples that do not conform to the expected normal patterns. UAD has two main advantages over its fully supervised counterpart. Firstly, it is able to directly leverage large datasets available from health screening programs that contain mostly normal image samples, avoiding the costly manual la

Flustered

Conditional Training with Bounding Map for Universal Lesion Detectionby coarse-to-fine two-stage detection approaches, but such two-stage ULD methods still suffer from issues like imbalance of positive v.s. negative anchors during object proposal and insufficient supervision problem during localization regression and classification of the region of interest (RoI) pro