Consistency learning combined with pseudo label training is a mainstream paradigm in semi-supervised medical image segmentation (SSMIS), yet it faces two challenges: 1)The one-sided search and handling of uncertainty regions make it difficult to address complex uncertainty situations caused by multiple networks. 2)The premature reaching of consensus in traditional SSMIS frameworks hinders the information complementarity between sub-networks. To address these, we propose a multi-view interactive interference training framework based on cross-network uncertainty (MIICU). Specifically, we design a cross-network uncertainty region searching (CUS) module, which combines the predictions from two networks to provide more reasonable uncertainty regions. Then, we design a dual-decision complementary displacement (DCD) strategy that performs displacement operations on cross-network uncertainty regions in different scenarios, so as to facilitate the learning of these regions. We further propose a multi-view interactive interference training strategy by expanding the training environment with three different concatenation forms, encouraging information complementarity between sub-networks. Evaluation on the TN3K and ACDC datasets shows that our approach outperforms existing SSMIS methods and is comparable to fully supervised methods. Code has been released at GitHub

Federated learning enables a server to train a global model by leveraging data distributed across multiple clients. However, its inherently distributed and iterative nature introduces significant communication overhead as well as potential privacy concerns. To mitigate these issues, one-shot federated learning restricts communication between the server and clients to a single round. Nevertheless, this constraint often results in reduced accuracy. In the field of medical diagnosis, data security is particularly critical and has become a major research focus. In this paper, we propose a one-shot federated learning framework and conduct experiments on three popular datasets and a melanoma image dataset collected by ourselves. To further enhance performance, we introduce One-shot Federated Learning framework for Melanoma Diagnosis, OFL-MD, which incorporates a pre-trained diffusion model CLIP to assist clients in generating synthetic datasets and employs differential privacy to further enhance the privacy. The synthetic datasets are then transmitted to the global model for lightweight fine-tuning, thereby improving accuracy. We evaluate our approach on both widely used benchmark datasets and our own melanoma dataset. The results validate the effectiveness of our multimodal one-shot federated learning framework, which not only preserves data privacy and achieves high global model accuracy but also shows promise for future deployment in assisting dermatologists with melanoma detection of patients.

Zero-shot medical image diagnosis is driven by vision-language pretraining on large-scale image-text pairs. However, aligning chest X-rays with reports remains challenging, particularly when multiple abnormalities with overlapping visual features lead to ambiguous correspondences. To address this challenge, we propose MedCMCL-VLP, a novel vision-language pretraining framework that simulates the hierarchical learning process of medical professionals. The framework consists of two key components: (1) a new enhanced Vision Mamba encoder with a spatial adaptor to capture long-range spatial dependencies and small-scale abnormalities in high-resolution chest X-rays and (2) a knowledge-guided text encoder that integrates medical domain knowledge with large language models (LLMs) to capture fine-grained semantic information from radiology reports. More importantly, MedCMCL-VLP employs a two-phase training paradigm that guides the model from novice to expert levels, effectively improving diagnostic accuracy. We evaluate MedCMCL-VLP on five public chest X-ray datasets. The experimental results show that our framework achieves state-of-the-art performance in both zero-shot and fine-tuned classification tasks.

Super-resolution plays a crucial role in enhancing the diagnostic utility of ultrasound images by recovering fine anatomical structures. Due to their remarkable performance in natural image restoration and generation, diffusion probabilistic models have become attractive for ultrasound image superresolution that highly requires complex textures and structural details modelling. However, existing diffusion-based superresolution methods typically apply uniform noise perturbations and reconstruction strategies across the entire image. Such global treatment neglects the varying reconstruction difficulty and clinical importance of different regions, often resulting in blurry lesion boundaries and suboptimal recovery of diagnostically significant structures. To address this issue, we propose UGDT-SR, a structure-aware ultrasound image super-resolution framework that integrates Bayesian uncertainty modeling and Transformer-guided structural representation into the diffusion process. Specifically, we introduce a Bayesian uncertainty network to estimate pixel-wise reconstruction difficulty, generating structural uncertainty masks that adaptively modulate local noise injection during forward diffusion. Furthermore, we inject these uncertainty masks into the conditional encoder to guide the model's attention toward clinically relevant regions throughout the reconstruction. Extensive experiments on both public and clinical ultrasound datasets demonstrate that UGDT-SR consistently outperformed CNN-, GAN-, and diffusion-based baselines, achieving superior perceptual quality and structural fidelity, especially in lesion areas.

Weakly supervised methods typically guided the pixel-wise training by comparing the predictions to single-level labels containing diverse segmentation-related information at once, but struggled to represent subtle feature differences between nodule and background regions and confused incorrect information, resulting in under-fitting or over-fitting in the segmentation predictions. This work proposes a weakly supervised network that generates multi-level labels from four-point annotation to refine diverse constraints for delicate nodule segmentation. The Distance-Similarity Fusion Prior referring to the points annotations filters out information irrelevant to nodules. The bounding box and pure foreground/background labels, generated from the point annotation, guarantee the rationality of the prediction in the arrangement of target localization and the spatial distribution of target/background regions, respectively. Our proposed network outperforms existing weakly supervised methods on two public datasets with respect to accuracy and robustness, improving the applicability of deep-learning-based segmentation in the clinical practice of thyroid nodule diagnosis.

Weakly-supervised learning methods have become increasingly attractive for medical image segmentation, but suffered from a high dependence on quantifying the pixel-wise affinities of low-level features, which are easily corrupted in thyroid ultrasound images, resulting in segmentation over-fitting to weakly annotated regions without precise delineation of target boundaries. We propose a dual-branch weakly-supervised learning framework to optimize the backbone segmentation network by calibrating semantic features into rational spatial distribution under the indirect, coarse guidance of the bounding box mask. Specifically, in the spatial arrangement consistency branch, the maximum activations sampled from the preliminary segmentation prediction and the bounding box mask along the horizontal and vertical dimensions are compared to measure the rationality of the approximate target localization. In the hierarchical prediction consistency branch, the target and background prototypes are encapsulated from the semantic features under the combined guidance of the preliminary segmentation prediction and the bounding box mask. The secondary segmentation prediction induced from the prototypes is compared with the preliminary prediction to quantify the rationality of the elaborated target and background semantic feature perception. Experiments on three thyroid datasets illustrate that our model outperforms existing weakly-supervised methods for thyroid gland and nodule segmentation and is comparable to the performance of fully-supervised methods with reduced annotation time. The proposed method has provided a weakly-supervised segmentation strategy by simultaneously considering the target's location and the rationality of target and background semantic features distribution. It can improve the applicability of deep learning based segmentation in the clinical practice.

Accurately predicting the response to neoadjuvant chemotherapy (NCT) is crucial for gastric cancer treatment planning. Multimodal diagnostic methods enhance prediction accuracy by integrating images and clinical features, but they often overlook intra-class differences, causing feature entanglement, and single models struggle to capture dataset diversity. To address these issues, we propose Clustering Multimodal Ensemble Learning (CMEL) framework, which aligns and fuses image features from different domains with clinical features and performs decision fusion using an ensemble model. Specifically, we design the clustering process using Siamese network and memory bank to partition images in feature space and introduce contrastive clustering loss to enhance clustering properties and reduce feature entanglement across domains. We design Hierarchical Feature Alignment Fusion (HFAF) module to generate multi-level image features at different depths and then separately align and fuse them with clinical features, providing diverse features to cover variations in the dataset. We design Dynamic Multi-Classifier Decision Fusion (DMDF) module to predict on fused features using a gating mechanism supervised by pseudo-labels for dynamic weights, enhancing prediction across different domains. On our collected the Gastric Cancer Chemotherapy Response (GCCR) dataset, CMEL achieves an AUC of 76.09%, accuracy of 72.00%, PPV of 86.02%. These results demonstrate the feasibility of joint image and clinical data in predicting NCT response and provide valuable insights for gastric cancer treatment. The code is available at https://github.com/WHX0259/CMEL.