The objective of dehazing images is to enhance a hazy image by removing the atmospheric haze and restoring it to a clear, haze-free image. Previous studies often used simple deep learning methods combined with dehazing physical models, while neglecting the significance behind the parameters of the physical models. They did not employ appropriate deep learning methods to learn the parameters, which in turn hindered the effective restoration of the color and texture details in the images. To address the above issues, we propose a physical perception network (PPnet) for color and texture recovery. PPnet includes the following two major parts: the High-low frequency physical perception module(FPP) and the Attention-based physical estimation module(APE). The FPP module employ trainable filters to produce feature maps at both high and low frequencies, which are then applied to two identical single-parameter dehazing physical models. This module takes into greater consideration the contours and detailed features of images at different frequencies, and corrects the images through a physical model. Furthermore, in the physical model, we find that different parameters represent different physical meanings. Some parameters express global features while others represent local features. We propose the APE module, which utilized the pixel and channel attention method to simulate the physical parameter representation. Thus, it more accurately considers the physical significance of the parameters. PPnet exhibits a notable enhancement on the SOTS dataset the and HSTS dataset compared to existing state-of-the-art techniques.

Abstract Doctors typically use non‐contrast‐enhanced computed tomography (NCECT) in the treatment of kidney cancer to map kidney and tumour structural information to functional imaging single‐photon emission computed tomography, which is then used to assess patient kidney function and predict postoperative recovery. However, the assessment of kidney function and formulation of surgical plans is constrained by the low contrast of tumours in NCECT, which hinders the acquisition of accurate tumour boundaries. Therefore, this study designed a radiomics feature‐enhanced level‐set evolution (RFLSE) to precisely segment small‐sample low‐contrast kidney tumours. Integration of high‐dimensional radiomics features into the level‐set energy function enhances the edge detection capability of low‐contrast kidney tumours. The use of sensitive radiomics features to control the regional term parameters achieves adaptive adjustment of the curve evolution amplitude, improving the level‐set segmentation process. The experimental data used low‐contrast, limited‐sample tumours provided by hospitals, as well as the public datasets BUSI18 and KiTS19. Comparative results with advanced energy functionals and deep learning models demonstrate the precision and robustness of RFLSE segmentation. Additionally, the application value of RFLSE in assisting doctors with accurately marking tumours and generating high‐quality pseudo‐labels for deep learning datasets is demonstrated.

Text-based visual question answering (VQA) requires to read and understand text in an image to correctly answer a given question. However, most current methods simply add optical character recognition (OCR) tokens extracted from the image into the VQA model without considering contextual information of OCR tokens and mining the relationships between OCR tokens and scene objects. In this paper, we propose a novel text-centered method called RUArt (Reading, Understanding and Answering the Related Text) for text-based VQA. Taking an image and a question as input, RUArt first reads the image and obtains text and scene objects. Then, it understands the question, OCRed text and objects in the context of the scene, and further mines the relationships among them. Finally, it answers the related text for the given question through text semantic matching and reasoning. We evaluate our RUArt on two text-based VQA benchmarks (ST-VQA and TextVQA) and conduct extensive ablation studies for exploring the reasons behind RUArt’s effectiveness. Experimental results demonstrate that our method can effectively explore the contextual information of the text and mine the stable relationships between the text and objects.

Text-based Visual Question Answering (Text-VQA) is a question-answering task to understand scene text, where the text is usually recognized by Optical Character Recognition (OCR) systems. However, the text from OCR systems often includes spelling errors, such as "pepsi" being recognized as "peosi". These OCR errors are one of the major challenges for Text-VQA systems. To address this, we propose a novel Text-VQA method to alleviate OCR errors via OCR token evolution. First, we artificially create the misspelled OCR tokens in the training time, and make the system more robust to the OCR errors. To be specific, we propose an OCR Token-Word Contrastive (TWC) learning task, which pre-trains word representation by augmenting OCR tokens via the Levenshtein distance between the OCR tokens and words in a dictionary. Second, by assuming that the majority of characters in misspelled OCR tokens are still correct, a multimodal transformer is proposed and fine-tuned to predict the answer using character-based word embedding. Specifically, we introduce a vocabulary predictor with character-level semantic matching, which enables the model to recover the correct word from the vocabulary even with misspelled OCR tokens. A variety of experimental evaluations show that our method outperforms the state-of-the-art methods on both TextVQA and ST-VQA datasets. The code will be released at https://github.com/xiaojino/TWA.

Text-based Visual Question Answering(VQA) usually needs to analyze and understand the text in a picture to give a correct answer for the given question. In this paper, a generic Text-based VQA with Knowledge Base (KB) is proposed, which performs text-based search on text information obtained by optical character recognition (OCR) in images, constructs task-oriented knowledge information and integrates it into the existing models. Due to the complexity of the image scene, the accuracy of OCR is not very high, and there are often cases where the words have individual character that is incorrect, resulting in inaccurate text information; here, some correct words can be found with help of KB, and the correct image text information can be added. Moreover, the knowledge information constructed with KB can better explain the image information, allowing the model to fully understand the image and find the appropriate text answer. The experimental results on the TextVQA dataset show that our method improves the accuracy, and the maximum increment is 39.2%.

MOTIVATION: With the abundant medical resources, especially literature available online, it is possible for people to understand their own health status and relevant problems autonomously. However, how to obtain the most appropriate answer from the increasingly large-scale database, remains a great challenge. Here, we present a biomedical question answering framework and implement a system, Health Assistant, to enable the search process. METHODS: In Health Assistant, a search engine is firstly designed to rank biomedical documents based on contents. Then various query processing and search techniques are utilized to find the relevant documents. Afterwards, the titles and abstracts of top-N documents are extracted to generate candidate snippets. Finally, our own designed query processing and retrieval approaches for short text are applied to locate the relevant snippets to answer the questions. RESULTS: Our system is evaluated on the BioASQ benchmark datasets, and experimental results demonstrate the effectiveness and robustness of our system, compared to BioASQ participant systems and some state-of-the-art methods on both document retrieval and snippet retrieval tasks. AVAILABILITY AND IMPLEMENTATION: A demo of our system is available at https://github.com/jinzanxia/biomedical-QA.