While Large Language Models (LLMs) show great promise in emulating clinicians' cognitive processes, ensuring the reliability of their reasoning remains unresolved. Existing chain-of-thought often entangles logic with supporting medical knowledge, forming an opaque “black box” that conceals errors and induces a critical failure mode we term retrospective knowledge forgery—where models fabricate facts posthoc to justify reasoning. To tackle this, we propose Diagno-S (Diagnosis-Separation), a framework that decouples reasoning and knowledge for diagnostic-oriented optimization. Diagno-S compels models to decompose reasoning into independently verifiable logical and factual steps, making hidden flaws traceable. Leveraging this transparency, we employ curriculum learning and a diagnostic module to detect and correct these errors via direct preference optimization. Diagno-S attains state-of-the-art results on seven medical QA benchmarks, including MedQA, PubMedQA, and MedXpert, and provides a systematic solution to knowledge forgery, advancing trustworthy medical AI.

To scale out the massive metadata access, the Ceph distributed file system (CephFS) adopts a dynamic subtree partitioning method, splitting the hierarchical namespace and distributing subtrees across multiple metadata servers. However, this method suffers from a severe imbalance problem that may result in poor performance due to its inaccurate imbalance prediction, ignorance of workload characteristics, and unnecessary/invalid migration activities. To eliminate these inefficiencies, we propose Lunule, a novel CephFS metadata load balancer, which employs an imbalance factor model for accurately determining when to trigger re-balance and tolerate unharmful imbalanced situations. Lunule further adopts a workload-aware migration planner to appropriately select subtree migration candidates. Finally, we extend Lunule to Lunule + , which models metadata accesses into matrices, and employs matrix-based formulas for more accurate load prediction and re-balance decision. Compared to baselines, Lunule achieves better load balance, increases the metadata throughput by up to 315.8%, and shortens the tail job completion time by up to 64.6% for five real-world workloads and their mixture, respectively. Besides, Lunule is capable of handling the metadata cluster expansion and the workload growth, and scales linearly on a 16-node cluster. Compared to Lunule, Lunule + achieves up to 64.96% better metadata load balance, and 13.53-86.09% higher throughput.

Refining the seed region to obtain finely annotated pseudo masks for training a segmentation model is a crucial step in the multi-stage weakly supervised semantic segmentation (WSSS) framework. One of the most popular refinement methods, IRN, extends seed regions towards the edges in the image. However, we observed that, due to the lack of guidance from semantic information, IRN’s refinement may lead the generation of partially erroneous refinement directions. To address this issue, we leverage prototypes to recover the overlooked category semantic information in the refinement stage. We propose a prototype-based pseudo mask reclassification post-processing (PtReCl) to correct misclassified pixels in the pseudo masks, generating refined pseudo masks with more accurate coverage. Experimental evaluations demonstrate that our post-processing approach brings improvements in both pseudo mask quality and segmentation results on PASCAL VOC and MS COCO datasets, achieving state-of-the-art performance on VOC.

Generating class activation maps (CAM) as seed regions is a crucial step in weakly supervised semantic segmentation (WSSS). During the training of classification models, instance area information is incorporated into feature vectors and learned by the network. However, during CAM generation, pixel-level prediction scores are solely dependent on features and classifier weights, neglecting the area factor. Such area information becomes a source of bias between training and CAM generation, leading to a significant under-activation (false negatives) of foreground regions. To address this issue, we propose a class area intervention strategy to fine-tune the classification model and rectify this bias. Additionally, we employ a partition activation strategy that can simultaneously consider both under activation and over-activation to generate more precise AICAMs. AICAM is applicable to various WSSS methods based on CAM or its variants. The evaluations on both the PASCAL VOC and MS COCO datasets demonstrate that our method enhances multiple WSSS techniques.

It is well known that weakly supervised semantic segmentation requires only image-level labels for training, which greatly reduces the annotation cost. In recent years, prototype-based approaches, which prove to substantially improve the segmentation performance, have been favored by a wide range of researchers. However, we are surprised to find that there are semantic gaps between different regions within the same object, hindering the optimization of prototypes, so the traditional prototypes can not adequately represent the entire object. Therefore, we propose region-specific prototypes to adaptively describe the regions themselves, which alleviate the effect of semantic gap by separately obtaining prototypes for different regions of an object. In addition, to obtain more representative region-specific prototypes, a plug-and-play Spatially Fused Attention Module is proposed for combining the spatial correlation and the scale correlation of hierarchical features. Extensive experiments are conducted on PASCAL VOC 2012 and MS COCO 2014, and the results show that our method achieves state-of-the-art performance using only image-level labels.

In recent years, computer-aided diagnosis technology has made breakthroughs in clinical medicine. However, due to the different models and configurations of ultrasound instruments, the thyroid ultrasound images collected by different medical centers have different visual characteristics. It leads to domain shift in multi-center data and the lower generalization of computer-aided diagnosis models. We consider this limitation may attribute to the minor inter-class differences in thyroid ultrasound images, resulting in confusion space before and after domain feature alignment. Therefore, we propose an adversarial domain adaptation network with enhanced feature discriminability method. Among them, the discriminative feature learning module strengthens the discriminability of the learned features, so that the class distribution in each domain presents a high cohesion and low coupling effect; the class alignment module reduces the distance between the same class samples across domains to achieve class alignment and further strengthen the discriminability of each class. Experiments show that our method outperforms other state-of-the-art algorithms on the internal thyroid ultrasound image dataset. The method can effectively improve the model's generalization ability on multi-center thyroid ultrasound images.

It is difficult and expensive to obtain labels for image semantic segmentation tasks. This has led to more and more researches focusing on weakly supervised semantic segmentation (WSSS) with image-level labels and the class activation maps (CAM) is often used to locate objects. The performance of WSSS methods largely depends on the accuracy of the generated CAMs, but current methods can only perform rough locali/ation, especially cannot fit CAM to the object’s edge well. Therefore, we propose the Edge Enhancement Network (EEN), which uses the shallow features of the network to enhance edge information. This allows us to obtain more accurate CAMs that fit the edges only through image-level labels. It greatly improves the accuracy of pseudo ground truth and easily reaches the level of current mainstream methods. After many experiments, it has reached 67.0% mIoU on the Pascal VOC2012 validation set, which exceeds the latest method under the same training settings.