Accurate segmentation of breast lesions is vital for diagnosing breast diseases. Masked image modeling (MIM) with random masking performs well in self-supervised learning but struggles in breast ultrasound segmentation due to (1) ambiguous representations from similar intensities near lesion boundaries and (2) a bias toward irrelevant regions. We propose UA-MAE, an uncertainty-aware masked autoencoder that uses pixel-wise uncertainty maps to dynamically select masking patches, prioritizing boundaries and morphologically relevant lesion areas. Experiments on two public datasets for pre-training and three for fine-tuning show UA-MAE outperforming four state-of-theart SSL methods and two supervised approaches in segmentation accuracy across diverse breast ultrasound images. The code is available at https://github.com/yXiangXiong/UA-MAE.

Three-dimensional organ and cancer segmentation based on multi-sequence MRI is crucial for assisting clinical diagnosis. However, current automated segmentation methods often focus on specific sequences, specific organs, and specific cancers, i.e., lack of generality. To address this issue, we propose a universal segmentation network for multi-sequence MRI (UniMRISegNet) that can segment multiple organs and cancers. UniMRISegNet features a shared encoder-decoder architecture equipped with contextual prompt generation (CPG) and prompt-conditioned dynamic convolution (PCDC) modules. The CPG module encodes sequence-specific, position-specific, and organ/cancer-specific text prompts as prior information to inform UniMRISegNet about the specific task to be executed. The PCDC module can adaptively generate model weights based on the assigned prompts, enhancing the segmentation capabilities of the UniMRISegNet for specific tasks. To mitigate discrepancies between different sequences of the same organ and capture similarities between related sequences, we design a novel loss function called Semantic-Aware Cosine Similarity Loss (SACSL), which integrates the cosine similarity of text embeddings to reconcile discrepancies and similarities between MRI sequences of the same organ. We created a large-scale annotated multi-sequence, multi-organ, and multi-cancer segmentation workflow (MSOCS), and demonstrated that our UniMRISegNet outperforms other universal networks and single-task networks on MSOCS. Furthermore, the universal weights from MSOCS can be transferred to never-before-seen downstream tasks, achieving superior performance compared to training from scratch.

Ultrasound is widely used in clinical practice due to its affordability, portability, and safety. However, current AI research often overlooks combined disease prediction and tissue segmentation. We propose UniUSNet, a universal framework for ultrasound image classification and segmentation. This model handles various ultrasound types, anatomical positions, and input formats, excelling in both segmentation and classification tasks. Trained on a comprehensive dataset with over 9.7K annotations from 7 distinct anatomical positions, our model matches state-of-the-art performance and surpasses single-dataset and ablated models. Zero-shot and fine-tuning experiments show strong generalization and adaptability with minimal fine-tuning. We plan to expand our dataset and refine the prompting mechanism, with model weights and code available at (https://github.com/Zehui-Lin/UniUSNet).

Contrast-enhanced computed tomography (CE-CT) is the gold standard for diagnosing aortic dissection (AD). However, contrast agents can cause allergic reactions or renal failure in some patients. Moreover, AD diagnosis by radiologists using non-contrast-enhanced CT (NCE-CT) images has poor sensitivity. To address this issue, we propose a novel cascaded multi-task generative framework for AD detection using NCE-CT volumes. The framework includes a 3D nnU-Net and a 3D multi-task generative architecture (3D MTGA). Specifically, the 3D nnU-Net was employed to segment aortas from NCE-CT volumes. The 3D MTGA was then employed to simultaneously synthesize CE-CT volumes, segment true & false lumen, and classify the patient as AD or non-AD. A theoretical formulation demonstrated that the 3D MTGA could increase the Jensen-Shannon Divergence (JSD) between AD and non-AD for each NCE-CT volume, thus indirectly improving the AD detection performance. Experiments also showed that the proposed framework could achieve an average accuracy of 0.831, a sensitivity of 0.938, and an F1-score of 0.847 in comparison with seven state-of-the-art classification models used by three radiologists with junior, intermediate, and senior experiences, respectively. The experimental results indicate that the proposed framework obtains superior performance to state-of-the-art models in AD detection. Thus, it has great potential to reduce the misdiagnosis of AD using NCE-CT in clinical practice. The source codes and supplementary materials for our framework are available at https://github.com/yXiangXiong/CMTGF.