To tackle the issue of excessive parameter volumes during fine-tuning of large-scale pre-trained models with full parameters, Parameter-Efficient Fine-Tuning (PEFT) methods have been introduced. The core concept involves freezing the backbone network of the model and updating only a small subset of parameters. This strategy not only decreases the number of parameters needed for training but also delivers performance comparable to Full-Tuning, even surpassing it on certain datasets. However, most popular PEFT methods introduce extra parameters or modules for fine-tuning, which come with inherent limitations. In response, we propose a straightforward and efficient PEFT method called You Can Only Tune Normalization (YONO). YONO focuses solely on tuning the normalization layer and the final classification layer of the model. This method avoids adding extra modules, making it easily applicable to any model without causing inference delays. We extensively tested YONO on 28 benchmark datasets, and the results indicate that it requires significantly fewer parameters compared to other advanced PEFT methods. Additionally, we validated YONO’s efficiency and generalizability across various vision models. Finally, we further explore the essence of PEFT methods, whether they learn new knowledge or expose the capabilities that a model has already learned. Our findings suggest that YONO is more sensitive to improvements in dataset quality, making it a promising candidate for future scaling to larger models.

Large Language Models (LLMs) have shown impressive capabilities in code generation. However, they often struggle in complex programming scenarios due to incomplete semantic understanding and limited ability to correct misunderstandinginduced errors. While recent efforts have incorporated test cases to guide task comprehension, they typically overlook the quality and relevance of the test cases, reducing their effectiveness in steering accurate code generation. To overcome these limitations, we present PriGen, a multiagent collaborative framework for prioritized and test case driven code generation. PriGen introduces a novel test case prioritization mechanism that selects a high-value subset based on semantic coverage, boundary sensitivity, and error-triggering potential. These curated test cases assist in refining the LLM’s task understanding. Additionally, PriGen integrates a Cognitive Validation Loop, which iteratively verifies and improves the model’s comprehension through interactive evaluation and dynamic test injection, ensuring semantic alignment before code synthesis. We evaluate PriGen on two enhanced benchmarks, HumanEvalET and MBPP-ET, using three representative open-source LLMs: DeepSeek-Coder, Qwen2.5-Coder, and Llama-3.1. Experimental results show that PriGen consistently outperforms state-of-the-art baselines in both correctness and efficiency, demonstrating its effectiveness and generalizability in enhancing LLM-based code generation.

Unsupervised image segmentation aims to match low-level visual features with semantic-level representations without outer supervision. In this paper, we address the critical properties from the view of feature alignments and feature uniformity for UISS models. We also make a comparison between UISS and image-wise representation learning. Based on the analysis, we argue that the existing MI-based methods in UISS suffer from representation collapse. By this, we proposed a robust network called Semantic Attention Network(SAN), in which a new module Semantic Attention(SEAT) is proposed to generate pixel-wise and semantic features dynamically. Experimental results on multiple semantic segmentation benchmarks show that our unsupervised segmentation framework specializes in catching semantic representations, which outperforms all the unpretrained and even several pretrained methods.

The pancreatic disease taxonomy includes ten types of masses (tumors or cysts) [20], [8]. Previous work focuses on developing segmentation or classification methods only for certain mass types. Differential diagnosis of all mass types is clinically highly desirable [20] but has not been investigated using an automated image understanding approach.We exploit the feasibility to distinguish pancreatic ductal adenocarcinoma (PDAC) from the nine other nonPDAC masses using multi-phase CT imaging. Both image appearance and the 3D organ-mass geometry relationship are critical. We propose a holistic segmentation-mesh-classification network (SMCN) to provide patient-level diagnosis, by fully utilizing the geometry and location information, which is accomplished by combining the anatomical structure and the semantic detection-by-segmentation network. SMCN learns the pancreas and mass segmentation task and builds an anatomical correspondence-aware organ mesh model by progressively deforming a pancreas prototype on the raw segmentation mask (i.e., mask-to-mesh). A new graph-based residual convolutional network (Graph-ResNet), whose nodes fuse the information of the mesh model and feature vectors extracted from the segmentation network, is developed to produce the patient-level differential classification results. Extensive experiments on 661 patients’ CT scans (five phases per patient) show that SMCN can improve the mass segmentation and detection accuracy compared to the strong baseline method nnUNet (e.g., for nonPDAC, Dice: 0.611 vs. 0.478; detection rate: 89% vs. 70%), achieve similar sensitivity and specificity in differentiating PDAC and nonPDAC as expert radiologists (i.e., 94% and 90%), and obtain results comparable to a multimodality test [20] that combines clinical, imaging, and molecular testing for clinical management of patients.

Many existing approaches for mammogram analysis are based on single view. Some recent DNN-based multi-view approaches can perform either bilateral or ipsilateral analysis, while in practice, radiologists use both to achieve the best clinical outcome. MommiNet is the first DNN-based tri-view mass identification approach, which can simultaneously perform bilateral and ipsilateral analysis of mammographic images, and in turn, can fully emulate the radiologists' reading practice. In this paper, we present MommiNet-v2, with improved network architecture and performance. Novel high-resolution network (HRNet)-based architectures are proposed to learn the symmetry and geometry constraints, to fully aggregate the information from all views for accurate mass detection. A multi-task learning scheme is adopted to incorporate both Breast Imaging-Reporting and Data System (BI-RADS) and biopsy information to train a mass malignancy classification network. Extensive experiments have been conducted on the public DDSM (Digital Database for Screening Mammography) dataset and our in-house dataset, and state-of-the-art results have been achieved in terms of mass detection accuracy. Satisfactory mass malignancy classification result has also been obtained on our in-house dataset.

Breast Ultrasound (BUS) imaging has been recognized as an essential imaging modality for breast masses classification in China. Current deep learning (DL) based solutions for BUS classification seek to feed ultrasound (US) images into deep convolutional neural networks (CNNs), to learn a hierarchical combination of features for discriminating malignant and benign masses. One existing problem in current DL-based BUS classification was the lack of spatial and channel-wise features weighting, which inevitably allow interference from redundant features and low sensitivity. In this study, we aim to incorporate the instructive information provided by breast imaging reporting and data system (BI-RADS) within DL-based classification. A novel DL-based BI-RADS Vector-Attention Network (BVA Net) that trains with both texture information and decoded information from BI-RADS stratifications was proposed for the task. Three baseline models, pre-trained DenseNet-121, ResNet-50 and Residual-Attention Network (RA Net) were included for comparison. Experiments were conducted on a large scale private main dataset and two public datasets, UDIAT and BUSI. On the main dataset, BVA Net outperformed other models, in terms of AUC (area under the receiver operating curve, 0.908), ACC (accuracy, 0.865), sensitivity (0.812) and precision (0.795). BVA Net also achieved the high AUC (0.87 and 0.882) and ACC (0.859 and 0.843), on UDIAT and BUSI. Moreover, we proposed a method that integrates both BVA Net binary classification and BI-RADS stratification estimation, called integrated classification. The introduction of integrated classification helped improving the overall sensitivity while maintaining a high specificity.

Large-scale datasets with high-quality labels are desired for training accurate deep learning models. However, due to the annotation cost, datasets in medical imaging are often either partially-labeled or small. For example, DeepLesion is such a large-scale CT image dataset with lesions of various types, but it also has many unlabeled lesions (missing annotations). When training a lesion detector on a partially-labeled dataset, the missing annotations will generate incorrect negative signals and degrade the performance. Besides DeepLesion, there are several small single-type datasets, such as LUNA for lung nodules and LiTS for liver tumors. These datasets have heterogeneous label scopes, i.e., different lesion types are labeled in different datasets with other types ignored. In this work, we aim to develop a universal lesion detection algorithm to detect a variety of lesions. The problem of heterogeneous and partial labels is tackled. First, we build a simple yet effective lesion detection framework named Lesion ENSemble (LENS). LENS can efficiently learn from multiple heterogeneous lesion datasets in a multi-task fashion and leverage their synergy by proposal fusion. Next, we propose strategies to mine missing annotations from partially-labeled datasets by exploiting clinical prior knowledge and cross-dataset knowledge transfer. Finally, we train our framework on four public lesion datasets and evaluate it on 800 manually-labeled sub-volumes in DeepLesion. Our method brings a relative improvement of 49% compared to the current state-of-the-art approach in the metric of average sensitivity. We have publicly released our manual 3D annotations of DeepLesion online.11https://github.com/viggin/DeepLesion_manual_test_set

Mammography plays an essential role in early detection of breast cancer. Interpreting mammography is a professional task that requires well-trained radiologists with longtime clinical experience. In this paper, we present MABEL, an artificial intelligence-powered system to assist doctors for breast cancer screening and diagnosis in mammograms, in order to reduce their workloads and accelerate the diagnostic process. Our system smoothly integrates our upgraded lesion identification models, provides a doctor-oriented annotation tool and web interface, and can communicate with Picture Archiving and Communication System (PACS) in our collaborative hospital. Our lesion identification performance is evaluated on both public and in-house datasets, in which mass detection has achieved state-of-the-art accuracy in the single-view manner. The overall high satisfaction from doctors of our system is also demonstrated.