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

MOTTO

Toward Clinically Assisted Colorectal Polyp Recognition via Structured Cross-Modal Representation Co token and patch tokens for a specific modality image. By aligning the class tokens and spatial attention maps of paired NBI and WL images at different levels, the Transformer achieves the ability to keep both global and local representation consistency for the above two modalities. Extensive experi

Ganglion

TGANet: Text-Guided Attention for Improved Polyp Segmentationrove the overall performance of the model compared to state-of-the-art segmentation methods. We explore four different datasets and provide insights for size-specific improvements. Our proposed . (TGANet) can generalize well to variable-sized polyps in different datasets. Codes are available at ..

使虚弱

GLUT

0302-9743 ational Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which was held in Singapore in September 2022..The 574 revised full papers presented were carefully reviewed and selected from 1831 submissions in a double-blind review process. The papers are organized in

Vaginismus

咯咯笑

MOTIF

Deep is a Luxury We Don’t Haveer cost. We evaluate HCT using a high resolution mammography dataset. HCT is significantly superior to its CNN counterpart. Furthermore, we demonstrate HCT’s fitness for medical images by evaluating its effective receptive field. Code available at ..

Hallmark

Stay Focused - Enhancing Model Interpretability Through Guided Feature Trainingnable AI methods, we propose a new metric evaluating the focus with regards to a mask of the region of interest. Further, we are able to show that the resulting model is more robust against changes in the background by focusing the features onto the important areas of the scene and therefore improve model generalization.

禁止

lethal

Knowledge Distillation to Ensemble Global and Interpretable Prototype-Based Mammogram Classification with limited interpretability, which is a key barrier to their successful translation into clinical practice. On the other hand, prototype-based models improve interpretability by associating predictions with training image prototypes, but they are less accurate than global models and their prototy