MedFMC: A Real-world Dataset and Benchmark For Foundation Model Adaptation in Medical Image Classification

Foundation models, often pre-trained with large-scale data, have achieved paramount success in jump-starting various vision and language applications. Recent advances further enable adapting foundation models in downstream tasks efficiently using only a few training samples, e.g., in-context learning. Yet, the application of such learning paradigms in medical image analysis remains scarce due to the shortage of publicly accessible data and benchmarks. In this paper, we aim at approaches adapting the foundation models for medical image classification and present a novel dataset and benchmark for the evaluation, i.e., examining the overall performance of accommodating the large-scale foundation models downstream on a set of diverse real-world clinical tasks. We collect five sets of medical imaging data from multiple institutes targeting a variety of real-world clinical tasks (22,349 images in total), i.e., thoracic diseases screening in X-rays, pathological lesion tissue screening, lesion detection in endoscopy images, neonatal jaundice evaluation, and diabetic retinopathy grading. Results of multiple baseline methods are demonstrated using the proposed dataset from both accuracy and cost-effective perspectives.

Deblurring Masked Autoencoder is Better Recipe for Ultrasound Image Recognition

Masked autoencoder (MAE) has attracted unprecedented attention and achieves remarkable performance in many vision tasks. It reconstructs random masked image patches (known as proxy task) during pretraining and learns meaningful semantic representations that can be transferred to downstream tasks. However, MAE has not been thoroughly explored in ultrasound imaging. In this work, we investigate the potential of MAE for ultrasound image recognition. Motivated by the unique property of ultrasound imaging in high noise-to-signal ratio, we propose a novel deblurring MAE approach that incorporates deblurring into the proxy task during pretraining. The addition of deblurring facilitates the pretraining to better recover the subtle details presented in the ultrasound images, thus improving the performance of the downstream classification task. Our experimental results demonstrate the effectiveness of our deblurring MAE, achieving state-of-the-art performance in ultrasound image classification. Overall, our work highlights the potential of MAE for ultrasound image recognition and presents a novel approach that incorporates deblurring to further improve its effectiveness.

ConES: Concept Embedding Search for Parameter Efficient Tuning Large Vision Language Models

Large pre-trained vision-language models have shown great prominence in transferring pre-acquired knowledge to various domains and downstream tasks with appropriate prompting or tuning. Existing prevalent tuning methods can be generally categorized into three genres: 1) prompt engineering by creating suitable prompt texts, which is time-consuming and requires domain expertise; 2) or simply fine-tuning the whole model, which is extremely inefficient; 3) prompt tuning through parameterized prompt embeddings with the text encoder. Nevertheless, all methods rely on the text encoder for bridging the modality gap between vision and language. In this work, we question the necessity of the cumbersome text encoder for a more lightweight and efficient tuning paradigm as well as more representative prompt embeddings closer to the image representations. To achieve this, we propose a Concept Embedding Search (ConES) approach by optimizing prompt embeddings -- without the need of the text encoder -- to capture the 'concept' of the image modality through a variety of task objectives. By dropping the text encoder, we are able to significantly speed up the learning process, \eg, from about an hour to just ten minutes in our experiments for personalized text-to-image generation without impairing the generation quality. Moreover, our proposed approach is orthogonal to current existing tuning methods since the searched concept embeddings can be further utilized in the next stage of fine-tuning the pre-trained large models for boosting performance. Extensive experiments show that our approach can beat the prompt tuning and textual inversion methods in a variety of downstream tasks including objection detection, instance segmentation, and image generation. Our approach also shows better generalization capability for unseen concepts in specialized domains, such as the medical domain.

Joint Uplink and Downlink Resource Allocation Towards Energy-efficient Transmission for URLLC

Ultra-reliable and low-latency communications (URLLC) is firstly proposed in 5G networks, and expected to support applications with the most stringent quality-of-service (QoS). However, since the wireless channels vary dynamically, the transmit power for ensuring the QoS requirements of URLLC may be very high, which conflicts with the power limitation of a real system. To fulfill the successful URLLC transmission with finite transmit power, we propose an energy-efficient packet delivery mechanism incorparated with frequency-hopping and proactive dropping in this paper. To reduce uplink outage probability, frequency-hopping provides more chances for transmission so that the failure hardly occurs. To avoid downlink outage from queue clearing, proactive dropping controls overall reliability by introducing an extra error component. With the proposed packet delivery mechanism, we jointly optimize bandwidth allocation and power control of uplink and downlink, antenna configuration, and subchannel assignment to minimize the average total power under the constraint of URLLC transmission requirements. Via theoretical analysis (e.g., the convexity with respect to bandwidth, the independence of bandwidth allocation, the convexity of antenna configuration with inactive constraints), the simplication of finding the global optimal solution for resource allocation is addressed. A three-step method is then proposed to find the optimal solution for resource allocation. Simulation results validate the analysis and show the performance gain by optimizing resource allocation with the proposed packet delivery mechanism.

MedGPTEval: A Dataset and Benchmark to Evaluate Responses of Large Language Models in Medicine

METHODS: First, a set of evaluation criteria is designed based on a comprehensive literature review. Second, existing candidate criteria are optimized for using a Delphi method by five experts in medicine and engineering. Third, three clinical experts design a set of medical datasets to interact with LLMs. Finally, benchmarking experiments are conducted on the datasets. The responses generated by chatbots based on LLMs are recorded for blind evaluations by five licensed medical experts. RESULTS: The obtained evaluation criteria cover medical professional capabilities, social comprehensive capabilities, contextual capabilities, and computational robustness, with sixteen detailed indicators. The medical datasets include twenty-seven medical dialogues and seven case reports in Chinese. Three chatbots are evaluated, ChatGPT by OpenAI, ERNIE Bot by Baidu Inc., and Doctor PuJiang (Dr. PJ) by Shanghai Artificial Intelligence Laboratory. Experimental results show that Dr. PJ outperforms ChatGPT and ERNIE Bot in both multiple-turn medical dialogue and case report scenarios.

SAM on Medical Images: A Comprehensive Study on Three Prompt Modes

The Segment Anything Model (SAM) made an eye-catching debut recently and inspired many researchers to explore its potential and limitation in terms of zero-shot generalization capability. As the first promptable foundation model for segmentation tasks, it was trained on a large dataset with an unprecedented number of images and annotations. This large-scale dataset and its promptable nature endow the model with strong zero-shot generalization. Although the SAM has shown competitive performance on several datasets, we still want to investigate its zero-shot generalization on medical images. As we know, the acquisition of medical image annotation usually requires a lot of effort from professional practitioners. Therefore, if there exists a foundation model that can give high-quality mask prediction simply based on a few point prompts, this model will undoubtedly become the game changer for medical image analysis. To evaluate whether SAM has the potential to become the foundation model for medical image segmentation tasks, we collected more than 12 public medical image datasets that cover various organs and modalities. We also explore what kind of prompt can lead to the best zero-shot performance with different modalities. Furthermore, we find that a pattern shows that the perturbation of the box size will significantly change the prediction accuracy. Finally, Extensive experiments show that the predicted mask quality varied a lot among different datasets. And providing proper prompts, such as bounding boxes, to the SAM will significantly increase its performance.

Understanding Overfitting in Adversarial Training via Kernel Regression

Adversarial training and data augmentation with noise are widely adopted techniques to enhance the performance of neural networks. This paper investigates adversarial training and data augmentation with noise in the context of regularized regression in a reproducing kernel Hilbert space (RKHS). We establish the limiting formula for these techniques as the attack and noise size, as well as the regularization parameter, tend to zero. Based on this limiting formula, we analyze specific scenarios and demonstrate that, without appropriate regularization, these two methods may have larger generalization error and Lipschitz constant than standard kernel regression. However, by selecting the appropriate regularization parameter, these two methods can outperform standard kernel regression and achieve smaller generalization error and Lipschitz constant. These findings support the empirical observations that adversarial training can lead to overfitting, and appropriate regularization methods, such as early stopping, can alleviate this issue.

Dealing With Heterogeneous 3D MR Knee Images: A Federated Few-Shot Learning Method With Dual Knowledge Distillation

Federated Learning has gained popularity among medical institutions since it enables collaborative training between clients (e.g., hospitals) without aggregating data. However, due to the high cost associated with creating annotations, especially for large 3D image datasets, clinical institutions do not have enough supervised data for training locally. Thus, the performance of the collaborative model is subpar under limited supervision. On the other hand, large institutions have the resources to compile data repositories with high-resolution images and labels. Therefore, individual clients can utilize the knowledge acquired in the public data repositories to mitigate the shortage of private annotated images. In this paper, we propose a federated few-shot learning method with dual knowledge distillation. This method allows joint training with limited annotations across clients without jeopardizing privacy. The supervised learning of the proposed method extracts features from limited labeled data in each client, while the unsupervised data is used to distill both feature and response-based knowledge from a national data repository to further improve the accuracy of the collaborative model and reduce the communication cost. Extensive evaluations are conducted on 3D magnetic resonance knee images from a private clinical dataset. Our proposed method shows superior performance and less training time than other semi-supervised federated learning methods. Codes and additional visualization results are available at https://github.com/hexiaoxiao-cs/fedml-knee.

HD-GCN:A Hybrid Diffusion Graph Convolutional Network

The information diffusion performance of GCN and its variant models is limited by the adjacency matrix, which can lower their performance. Therefore, we introduce a new framework for graph convolutional networks called Hybrid Diffusion-based Graph Convolutional Network (HD-GCN) to address the limitations of information diffusion caused by the adjacency matrix. In the HD-GCN framework, we initially utilize diffusion maps to facilitate the diffusion of information among nodes that are adjacent to each other in the feature space. This allows for the diffusion of information between similar points that may not have an adjacent relationship. Next, we utilize graph convolution to further propagate information among adjacent nodes after the diffusion maps, thereby enabling the spread of information among similar nodes that are adjacent in the graph. Finally, we employ the diffusion distances obtained through the use of diffusion maps to regularize and constrain the predicted labels of training nodes. This regularization method is then applied to the HD-GCN training, resulting in a smoother classification surface. The model proposed in this paper effectively overcomes the limitations of information diffusion imposed only by the adjacency matrix. HD-GCN utilizes hybrid diffusion by combining information diffusion between neighborhood nodes in the feature space and adjacent nodes in the adjacency matrix. This method allows for more comprehensive information propagation among nodes, resulting in improved model performance. We evaluated the performance of DM-GCN on three well-known citation network datasets and the results showed that the proposed framework is more effective than several graph-based semi-supervised learning methods.