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Abstract Medical images are difficult to comprehend for a person without expertise. The scarcity of medical practitioners across the globe often face the issue of physical and mental fatigue due to the high number of cases, inducing human errors during the diagnosis. In such scenarios, having an additional opinion can be helpful in boosting the confidence of the decision maker. Thus, it becomes crucial to have a reliable visual question answering (VQA) system to provide a ‘second opinion’ on medical cases. However, most of the VQA systems that work today cater to real-world problems and are not specifically tailored for handling medical images. Moreover, the VQA system for medical images needs to consider a limited amount of training data available in this domain. In this paper, we develop MedFuseNet , an attention-based multimodal deep learning model, for VQA on medical images taking the associated challenges into account. Our MedFuseNet aims at maximizing the learning with minimal complexity by breaking the problem statement into simpler tasks and predicting the answer. We tackle two types of answer prediction—categorization and generation. We conducted an extensive set of quantitative and qualitative analyses to evaluate the performance of MedFuseNet . Our experiments demonstrate that MedFuseNet outperforms the state-of-the-art VQA methods, and that visualization of the captured attentions showcases the intepretability of our model’s predicted results. 

Abstract A new class of conjugated macrocycle, the cyclo[4]thiophene[4]furan hexyl ester (C4TE4FE), is reported. This cycle consists of alternating α‐linked thiophene‐3‐ester and furan‐3‐ester repeat units, and was prepared in a single step using Suzuki–Miyaura cross‐coupling of a 2‐(thiophen‐2‐yl)furan monomer. The ester side groups help promote asynconformation of the heterocycles, which enables formation of the macrocycle. Cyclic voltammetry studies revealed that C4TE4FE could undergo multiple oxidations, so treatment with SbCl₅resulted in formation of the [C4TE4FE]²⁺dication. Computational work, paired with¹H NMR spectroscopy of the dication, revealed that the cycle becomes globally aromatic upon 2e⁻oxidation, as the annulene pathway along the outer ring becomes Hückel aromatic. The change in ring current for the cycle upon oxidation was clear from¹H NMR spectroscopy, as the protons of the thiophene and furan rings shifted downfield by nearly 6 ppm. This work highlights the potential of sequence control in furan‐based macrocycles to tune electronic properties.