Computer-aided diagnosis (CAD) technology can assist clinicians in evaluating liver lesions and intervening with treatment in time. Although CAD technology has advanced in recent years, the application scope of existing datasets remains relatively limited, typically supporting only single tasks, which has somewhat constrained the development of CAD technology. To address the above limitation, in this paper, we construct a multi-task liver dataset (LiMT) used for liver and tumor segmentation, multi-label lesion classification, and lesion detection based on arterial phase-enhanced computed tomography (CT), potentially providing an exploratory solution that is able to explore the correlation between tasks and does not need to worry about the heterogeneity between task-specific datasets during training. The dataset includes CT volumes from 150 different cases, comprising four types of liver diseases as well as normal cases. Each volume has been carefully annotated and calibrated by experienced clinicians. This public multi-task dataset may become a valuable resource for the medical imaging research community in the future. In addition, this paper not only provides relevant baseline experimental results but also reviews existing datasets and methods related to liver-related tasks.

Medical Visual Question Answering (Med-VQA) models often suffer from language bias, which relies on linguistic cues to predict answers instead of understanding image content, limiting model performance. To address this, we propose DeCI, a novel causally-inspired intervention scheme to mitigate language bias in Med-VQA tasks. Specifically, DeCI incorporates two debiasing modules: a keyword-based fine-grained debiasing module to eliminate spurious correlations between clinical terms and answers, and a keyword-guided visual enhancement module to focus on key regions without requiring manual annotations. Experimental results on two public datasets and their bias-sensitive variants demonstrate that DeCI outperforms existing state-of-the-art Med-VQA models, achieving significant improvements in accuracy.

The success of deep learning in 3D medical image segmentation hinges on training with a large dataset of fully annotated 3D volumes, which are difficult and time-consuming to acquire. Although recent foundation models (e.g., segment anything model, SAM) can utilize sparse annotations to reduce annotation costs, segmentation tasks involving organs and tissues with blurred boundaries remain challenging. To address this issue, we propose a region uncertainty estimation framework for Computed Tomography (CT) image segmentation using noisy labels. Specifically, we propose a sample-stratified training strategy that stratifies samples according to their varying quality labels, prioritizing confident and fine-grained information at each training stage. This sample-to-voxel level processing enables more reliable supervision information to propagate to noisy label data, thus effectively mitigating the impact of noisy annotations. Moreover, we further design a boundary-guided regional uncertainty estimation module that adapts sample hierarchical training to assist in evaluating sample confidence. Experiments conducted across multiple CT datasets demonstrate the superiority of our proposed method over several competitive approaches under various noise conditions. Our proposed reliable label propagation strategy not only significantly reduces the cost of medical image annotation and robust model training but also improves the segmentation performance in scenarios with imperfect annotations, thus paving the way towards the application of medical segmentation foundation models under low-resource and remote scenarios. Code will be available at https://github.com/KHan-UJS/NoisyLabel.

Accurate segmentation of organs and tumors is crucial for clinical diagnosis. Deep learning methods have been widely applied to various medical image segmentation tasks. However, these methods often suffer from the foreground and background class imbalance challenge when dealing with small regions of interest. To address this limitation, we propose a Wavelet Transform-based Distribution Discrepancy Maximization (WT-DDM) framework for medical image segmentation. Specifically, we first introduce a Distribution Discrepancy Maximization (DDM) module that makes the model on the foreground region from abundant irrelevant background. Then, the Wavelet Transform-based Feature Enhancement (WTFE) module was applied to mine the texture details of the foreground object. Experiments on multiple popular medical image segmentation datasets demonstrate that our framework yields highly competitive segmentation results.

In the field of medical image processing, accurate segmentation is of great importance to assist doctors in diagnosis. However, existing machine learning methods are hardly effective for medical image segmentation in the absence of large and accurate datasets. Existing methods of learning with noisy labels rarely try to explore the correlation between noisy and clean labels. We found that some error corrections are learnable in the process of noisy labels corrected by medical experts. In this work, we propose a novel method to improve the performance of medical image segmentation. The method consists of two main networks: segmentation network segments the image and label correction network records and learns the denoising process of noisy labels, denoises the noisy labels. In addition, we introduce a feature fusion branch between the two networks. We compare with several state-of-the-art methods which learning with noisy label on the gastric wall dataset and notice that our method has strong competitiveness.

Despite the substantial progress made by deep networks in the field of medical image segmentation, they generally require sufficient pixel-level annotated data for training. The scale of training data remains to be the main bottleneck to obtain a better deep segmentation model. Semi-supervised learning is an effective approach that alleviates the dependence on labeled data. However, most existing semi-supervised image segmentation methods usually do not generate high-quality pseudo labels to expand training dataset. In this paper, we propose a deep semi-supervised approach for liver CT image segmentation by expanding pseudo-labeling algorithm under the very low annotated-data paradigm. Specifically, the output features of labeled images from the pretrained network combine with corresponding pixel-level annotations to produce class representations according to the mean operation. Then pseudo labels of unlabeled images are generated by calculating the distances between unlabeled feature vectors and each class representation. To further improve the quality of pseudo labels, we adopt a series of operations to optimize pseudo labels. A more accurate segmentation network is obtained by expanding the training dataset and adjusting the contributions between supervised and unsupervised loss. Besides, the novel random patch based on prior locations is introduced for unlabeled images in the training procedure. Extensive experiments show our method has achieved more competitive results compared with other semi-supervised methods when fewer labeled slices of LiTS dataset are available.