Deep Learning with HM-VGG: AI Strategies for Multi-modal Image Analysis

This study introduces the Hybrid Multi-modal VGG (HM-VGG) model, a cutting-edge deep learning approach for the early diagnosis of glaucoma. The HM-VGG model utilizes an attention mechanism to process Visual Field (VF) data, enabling the extraction of key features that are vital for identifying early signs of glaucoma. Despite the common reliance on large annotated datasets, the HM-VGG model excels in scenarios with limited data, achieving remarkable results with small sample sizes. The model's performance is underscored by its high metrics in Precision, Accuracy, and F1-Score, indicating its potential for real-world application in glaucoma detection. The paper also discusses the challenges associated with ophthalmic image analysis, particularly the difficulty of obtaining large volumes of annotated data. It highlights the importance of moving beyond single-modality data, such as VF or Optical Coherence Tomography (OCT) images alone, to a multimodal approach that can provide a richer, more comprehensive dataset. This integration of different data types is shown to significantly enhance diagnostic accuracy. The HM- VGG model offers a promising tool for doctors, streamlining the diagnostic process and improving patient outcomes. Furthermore, its applicability extends to telemedicine and mobile healthcare, making diagnostic services more accessible. The research presented in this paper is a significant step forward in the field of medical image processing and has profound implications for clinical ophthalmology.

Deep Learning for Medical Text Processing: BERT Model Fine-Tuning and Comparative Study

This paper proposes a medical literature summary generation method based on the BERT model to address the challenges brought by the current explosion of medical information. By fine-tuning and optimizing the BERT model, we develop an efficient summary generation system that can quickly extract key information from medical literature and generate coherent, accurate summaries. In the experiment, we compared various models, including Seq-Seq, Attention, Transformer, and BERT, and demonstrated that the improved BERT model offers significant advantages in the Rouge and Recall metrics. Furthermore, the results of this study highlight the potential of knowledge distillation techniques to further enhance model performance. The system has demonstrated strong versatility and efficiency in practical applications, offering a reliable tool for the rapid screening and analysis of medical literature.

Hybrid Mask Generation for Infrared Small Target Detection with Single-Point Supervision

Single-frame infrared small target (SIRST) detection poses a significant challenge due to the requirement to discern minute targets amidst complex infrared background clutter. Recently, deep learning approaches have shown promising results in this domain. However, these methods heavily rely on extensive manual annotations, which are particularly cumbersome and resource-intensive for infrared small targets owing to their minute sizes. To address this limitation, we introduce a Hybrid Mask Generation (HMG) approach that recovers high-quality masks for each target from only a single-point label for network training. Specifically, our HMG approach consists of a handcrafted Points-to-Mask Generation strategy coupled with a pseudo mask updating strategy to recover and refine pseudo masks from point labels. The Points-to-Mask Generation strategy divides two distinct stages: Points-to-Box conversion, where individual point labels are transformed into bounding boxes, and subsequently, Box-to-Mask prediction, where these bounding boxes are elaborated into precise masks. The mask updating strategy integrates the complementary strengths of handcrafted and deep-learning algorithms to iteratively refine the initial pseudo masks. Experimental results across three datasets demonstrate that our method outperforms the existing methods for infrared small target detection with single-point supervision.

Integrating Medical Imaging and Clinical Reports Using Multimodal Deep Learning for Advanced Disease Analysis

In this paper, an innovative multi-modal deep learning model is proposed to deeply integrate heterogeneous information from medical images and clinical reports. First, for medical images, convolutional neural networks were used to extract high-dimensional features and capture key visual information such as focal details, texture and spatial distribution. Secondly, for clinical report text, a two-way long and short-term memory network combined with an attention mechanism is used for deep semantic understanding, and key statements related to the disease are accurately captured. The two features interact and integrate effectively through the designed multi-modal fusion layer to realize the joint representation learning of image and text. In the empirical study, we selected a large medical image database covering a variety of diseases, combined with corresponding clinical reports for model training and validation. The proposed multimodal deep learning model demonstrated substantial superiority in the realms of disease classification, lesion localization, and clinical description generation, as evidenced by the experimental results.

SYNC-CLIP: Synthetic Data Make CLIP Generalize Better in Data-Limited Scenarios

Prompt learning is a powerful technique for transferring Vision-Language Models (VLMs) such as CLIP to downstream tasks. However, the prompt-based methods that are fine-tuned solely with base classes may struggle to generalize to novel classes in open-vocabulary scenarios, especially when data are limited. To address this issue, we propose an innovative approach called SYNC-CLIP that leverages SYNthetiC data for enhancing the generalization capability of CLIP. Based on the observation of the distribution shift between the real and synthetic samples, we treat real and synthetic samples as distinct domains and propose to optimize separate domain prompts to capture domain-specific information, along with the shared visual prompts to preserve the semantic consistency between two domains. By aligning the cross-domain features, the synthetic data from novel classes can provide implicit guidance to rebalance the decision boundaries. Experimental results on three model generalization tasks demonstrate that our method performs very competitively across various benchmarks. Notably, SYNC-CLIP outperforms the state-of-the-art competitor PromptSRC by an average improvement of 3.0% on novel classes across 11 datasets in open-vocabulary scenarios.

TransFA: Transformer-based Representation for Face Attribute Evaluation

Face attribute evaluation plays an important role in video surveillance and face analysis. Although methods based on convolution neural networks have made great progress, they inevitably only deal with one local neighborhood with convolutions at a time. Besides, existing methods mostly regard face attribute evaluation as the individual multi-label classification task, ignoring the inherent relationship between semantic attributes and face identity information. In this paper, we propose a novel \textbf{trans}former-based representation for \textbf{f}ace \textbf{a}ttribute evaluation method (\textbf{TransFA}), which could effectively enhance the attribute discriminative representation learning in the context of attention mechanism. The multiple branches transformer is employed to explore the inter-correlation between different attributes in similar semantic regions for attribute feature learning. Specially, the hierarchical identity-constraint attribute loss is designed to train the end-to-end architecture, which could further integrate face identity discriminative information to boost performance. Experimental results on multiple face attribute benchmarks demonstrate that the proposed TransFA achieves superior performances compared with state-of-the-art methods.

Scalable Online Disease Diagnosis via Multi-Model-Fused Actor-Critic Reinforcement Learning

For those seeking healthcare advice online, AI based dialogue agents capable of interacting with patients to perform automatic disease diagnosis are a viable option. This application necessitates efficient inquiry of relevant disease symptoms in order to make accurate diagnosis recommendations. This can be formulated as a problem of sequential feature (symptom) selection and classification for which reinforcement learning (RL) approaches have been proposed as a natural solution. They perform well when the feature space is small, that is, the number of symptoms and diagnosable disease categories is limited, but they frequently fail in assignments with a large number of features. To address this challenge, we propose a Multi-Model-Fused Actor-Critic (MMF-AC) RL framework that consists of a generative actor network and a diagnostic critic network. The actor incorporates a Variational AutoEncoder (VAE) to model the uncertainty induced by partial observations of features, thereby facilitating in making appropriate inquiries. In the critic network, a supervised diagnosis model for disease predictions is involved to precisely estimate the state-value function. Furthermore, inspired by the medical concept of differential diagnosis, we combine the generative and diagnosis models to create a novel reward shaping mechanism to address the sparse reward problem in large search spaces. We conduct extensive experiments on both synthetic and real-world datasets for empirical evaluations. The results demonstrate that our approach outperforms state-of-the-art methods in terms of diagnostic accuracy and interaction efficiency while also being more effectively scalable to large search spaces. Besides, our method is adaptable to both categorical and continuous features, making it ideal for online applications.

FIT: a Fast and Accurate Framework for Solving Medical Inquiring and Diagnosing Tasks

Automatic self-diagnosis provides low-cost and accessible healthcare via an agent that queries the patient and makes predictions about possible diseases. From a machine learning perspective, symptom-based self-diagnosis can be viewed as a sequential feature selection and classification problem. Reinforcement learning methods have shown good performance in this task but often suffer from large search spaces and costly training. To address these problems, we propose a competitive framework, called FIT, which uses an information-theoretic reward to determine what data to collect next. FIT improves over previous information-based approaches by using a multimodal variational autoencoder (MVAE) model and a two-step sampling strategy for disease prediction. Furthermore, we propose novel methods to substantially reduce the computational cost of FIT to a level that is acceptable for practical online self-diagnosis. Our results in two simulated datasets show that FIT can effectively deal with large search space problems, outperforming existing baselines. Moreover, using two medical datasets, we show that FIT is a competitive alternative in real-world settings.