MedXplain-VQA: Multi-Component Explainable Medical Visual Question Answering

Explainability is critical for the clinical adoption of medical visual question answering (VQA) systems, as physicians require transparent reasoning to trust AI-generated diagnoses. We present MedXplain-VQA, a comprehensive framework integrating five explainable AI components to deliver interpretable medical image analysis. The framework leverages a fine-tuned BLIP-2 backbone, medical query reformulation, enhanced Grad-CAM attention, precise region extraction, and structured chain-of-thought reasoning via multi-modal language models. To evaluate the system, we introduce a medical-domain-specific framework replacing traditional NLP metrics with clinically relevant assessments, including terminology coverage, clinical structure quality, and attention region relevance. Experiments on 500 PathVQA histopathology samples demonstrate substantial improvements, with the enhanced system achieving a composite score of 0.683 compared to 0.378 for baseline methods, while maintaining high reasoning confidence (0.890). Our system identifies 3-5 diagnostically relevant regions per sample and generates structured explanations averaging 57 words with appropriate clinical terminology. Ablation studies reveal that query reformulation provides the most significant initial improvement, while chain-of-thought reasoning enables systematic diagnostic processes. These findings underscore the potential of MedXplain-VQA as a robust, explainable medical VQA system. Future work will focus on validation with medical experts and large-scale clinical datasets to ensure clinical readiness.

Beyond Traditional Approaches: Multi-Task Network for Breast Ultrasound Diagnosis

Breast Ultrasound plays a vital role in cancer diagnosis as a non-invasive approach with cost-effective. In recent years, with the development of deep learning, many CNN-based approaches have been widely researched in both tumor localization and cancer classification tasks. Even though previous single models achieved great performance in both tasks, these methods have some limitations in inference time, GPU requirement, and separate fine-tuning for each model. In this study, we aim to redesign and build end-to-end multi-task architecture to conduct both segmentation and classification. With our proposed approach, we achieved outstanding performance and time efficiency, with 79.8% and 86.4% in DeepLabV3+ architecture in the segmentation task.