13 accepted papers
Combinatorial explosion problem caused by dual inputs presents a critical challenge in Deformable Medical Image Registration (DMIR). Since DMIR processes two images simultaneously as input, the combination relationships between features grow exponentially, ultimately the model considers more irrelev
Clinical MRI contrast acquisition suffers from inefficient information yield, which presents as a mismatch between the risky and costly acquisition protocol and fixed and sparse acquisition sequence. Applying world models to simulate the contrast enhancement kinetics in human body enables continuous
Abstract visual reasoning (AVR) is a critical ability of humans, and it has been widely studied, but arithmetic visual reasoning, a unique task in AVR to reason over number sense, is less studied in the literature. To facilitate this research, we construct a Machine Number Reasoning (MNR) dataset to…
Abstract Visual Reasoning (AVR) entails discerning latent patterns in visual data and inferring underlying rules. Existing solutions often lack scalability and adaptability, as deep architectures tend to overfit training data, and static neural networks fail to dynamically capture diverse rules. To…
Domain adaptation (DA) for cardiac ultrasound image segmentation is clinically significant and valuable. However, previous domain adaptation methods are prone to be affected by the incomplete pseudo label and low-quality target to source images. Human-centric domain adaptation has great advantages o…
Multi-view data involves various data forms, such as multi-feature, multi-sequence and multimodal data, providing rich semantic information for downstream tasks. The inherent challenge of incomplete multi-view missing multi-label learning lies in how to effectively utilize limited supervision and in…
Contrastive learning (CL) has become a cornerstone of self-supervised pretraining (SSP) in foundation models; however, extending CL to pixel-wise representation--crucial for medical vision--remains an open problem. Standard CL formulates SSP as a binary optimization problem (binary CL) where the exc…
Abstract Visual Reasoning (AVR) has been widely studied in literature. Our study reveals that AVR models tend to rely on appearance matching rather than a genuine understanding of underlying rules. We hence develop a challenging benchmark, Multiple Complex Compositional Reasoning (MC2R), composed of…
Accurate segmentation of topological tubular structures, such as blood vessels and roads, is crucial in various fields, ensuring accuracy and efficiency in downstream tasks. However, many factors complicate the task, including thin local structures and variable global morphologies. In this work, we…
Learning inter-image similarity is crucial for 3D medical images self-supervised pre-training, due to their sharing of numerous same semantic regions. However, the lack of the semantic prior in metrics and the semantic-independent variation in 3D medical images make it challenging to get a reliable…
In tiny object detection, the main challenges are tiny objects’ weak feature responses and possible semantic disappearance in deep networks. To address the problems, we proposed an Instance-level, Scale-adaptive, Shape-preserved, and Semantic-consistent Supervision (I4S) module for better locating t…
The nature of thick-slice scanning causes severe inter-slice discontinuities of 3D medical images, and the vanilla 2D/3D convolutional neural networks (CNNs) fail to represent sparse inter-slice information and dense intra-slice information in a balanced way, leading to severe underfitting to inter-…
Deep learning-based medical image registration and segmentation joint models utilize the complementarity (augmentation data or weakly supervised data from registration, region constraints from segmentation) to bring mutual improvement in complex scene and few-shot situation. However, further adoptio…