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1. Concept Detection and Caption Prediction in ImageCLEFmedical Caption 2023 with Convolutional Neural Networks; Vision and Text-to-Text Transfer Transformers

   Hasan M ; Layode O ; Rahman M. ( October 2023 , Working Notes of the Conference and Labs of the Evaluation Forum (CLEF 2023))

   Aliannejadi, M ; Faggioli, G ; Ferro, N ; Vlachos, M. (Ed.)

   This work discusses the participation of CS_Morgan in the Concept Detection and Caption Prediction tasks of the ImageCLEFmedical 2023 Caption benchmark evaluation campaign. The goal of this task is to automatically identify relevant concepts and their locations in images, as well as generate coherent captions for the images. The dataset used for this task is a subset of the extended Radiology Objects in Context (ROCO) dataset. The implementation approach employed by us involved the use of pre-trained Convolutional Neural Networks (CNNs), Vision Transformer (ViT), and Text-to-Text Transfer Transformer (T5) architectures. These models were leveraged to handle the different aspects of the tasks, such as concept detection and caption generation. In the Concept Detection task, the objective was to classify multiple concepts associated with each image. We utilized several deep learning architectures with ‘sigmoid’ activation to enable multilabel classification using the Keras framework. We submitted a total of five (5) runs for this task, and the best run achieved an F1 score of 0.4834, indicating its effectiveness in detecting relevant concepts in the images. For the Caption Prediction task, we successfully submitted eight (8) runs. Our approach involved combining the ViT and T5 models to generate captions for the images. For the caption prediction task, the ranking is based on the BERTScore, and our best run achieved a score of 0.5819 based on generating captions using the fine-tuned T5 model from keywords generated using the pretrained ViT as the encoder. 

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2. fNIRS-based classification of mind-wandering with personalized window selection for multimodal learning interfaces

   https://doi.org/10.1007/s12193-020-00325-z  

   Liu, Ruixue ; Walker, Erin ; Friedman, Leah ; Arrington, Catherine M. ; Solovey, Erin T. ( June 2020 , Journal on Multimodal User Interfaces)

   Automatic detection of an individual’s mind-wandering state has implications for designing and evaluating engaging and effective learning interfaces. While it is difficult to differentiate whether an individual is mind-wandering or focusing on the task only based on externally observable behavior, brain-based sensing offers unique insights to internal states. To explore the feasibility, we conducted a study using functional near-infrared spectroscopy (fNIRS) and investigated machine learning classifiers to detect mind-wandering episodes based on fNIRS data, both on an individual level and a group level, specifically focusing on automated window selection to improve classification results. For individual-level classification, by using a moving window method combined with a linear discriminant classifier, we found the best windows for classification and achieved a mean F1-score of 74.8%. For group-level classification, we proposed an individual-based time window selection (ITWS) algorithm to incorporate individual differences in window selection. The algorithm first finds the best window for each individual by using embedded individual-level classifiers and then uses these windows from all participants to build the final classifier. The performance of the ITWS algorithm is evaluated when used with eXtreme gradient boosting, convolutional neural networks, and deep neural networks. Our results show that the proposed algorithm achieved significant improvement compared to the previous state of the art in terms of brain-based classification of mind-wandering, with an average F1-score of 73.2%. This builds a foundation for mind-wandering detection for both the evaluation of multimodal learning interfaces and for future attention-aware systems. 

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3. Using BERT Embeddings to Model Word Importance in Conversational Transcripts for Deaf and Hard of Hearing Users

   https://doi.org/10.18653/v1/2022.ltedi-1.5  

   Amin, Akhter Al ; Hassan, Saad ; Alm, Cecilia ; Huenerfauth, Matt ( January 2022 , Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion)

   Deaf and hard of hearing individuals regularly rely on captioning while watching live TV. Live TV captioning is evaluated by regulatory agencies using various caption evaluation metrics. However, caption evaluation metrics are often not informed by preferences of DHH users or how meaningful the captions are. There is a need to construct caption evaluation metrics that take the relative importance of words in transcript into account. We conducted correlation analysis between two types of word embeddings and human-annotated labelled word-importance scores in existing corpus. We found that normalized contextualized word embeddings generated using BERT correlated better with manually annotated importance scores than word2vec-based word embeddings. We make available a pairing of word embeddings and their human-annotated importance scores. We also provide proof-of-concept utility by training word importance models, achieving an F1-score of 0.57 in the 6-class word importance classification task. 

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4. Identify diabetic retinopathy-related clinical concepts and their attributes using transformer-based natural language processing methods

   https://doi.org/10.1186/s12911-022-01996-2  

   Yu, Zehao ; Yang, Xi ; Sweeting, Gianna L. ; Ma, Yinghan ; Stolte, Skylar E. ; Fang, Ruogu ; Wu, Yonghui ( September 2022 , BMC Medical Informatics and Decision Making)

   Abstract Background Diabetic retinopathy (DR) is a leading cause of blindness in American adults. If detected, DR can be treated to prevent further damage causing blindness. There is an increasing interest in developing artificial intelligence (AI) technologies to help detect DR using electronic health records. The lesion-related information documented in fundus image reports is a valuable resource that could help diagnoses of DR in clinical decision support systems. However, most studies for AI-based DR diagnoses are mainly based on medical images; there is limited studies to explore the lesion-related information captured in the free text image reports. Methods In this study, we examined two state-of-the-art transformer-based natural language processing (NLP) models, including BERT and RoBERTa, compared them with a recurrent neural network implemented using Long short-term memory (LSTM) to extract DR-related concepts from clinical narratives. We identified four different categories of DR-related clinical concepts including lesions, eye parts, laterality, and severity, developed annotation guidelines, annotated a DR-corpus of 536 image reports, and developed transformer-based NLP models for clinical concept extraction and relation extraction. We also examined the relation extraction under two settings including ‘gold-standard’ setting—where gold-standard concepts were used–and end-to-end setting. Results For concept extraction, the BERT model pretrained with the MIMIC III dataset achieve the best performance (0.9503 and 0.9645 for strict/lenient evaluation). For relation extraction, BERT model pretrained using general English text achieved the best strict/lenient F1-score of 0.9316. The end-to-end system, BERT_general_e2e, achieved the best strict/lenient F1-score of 0.8578 and 0.8881, respectively. Another end-to-end system based on the RoBERTa architecture, RoBERTa_general_e2e, also achieved the same performance as BERT_general_e2e in strict scores. Conclusions This study demonstrated the efficiency of transformer-based NLP models for clinical concept extraction and relation extraction. Our results show that it’s necessary to pretrain transformer models using clinical text to optimize the performance for clinical concept extraction. Whereas, for relation extraction, transformers pretrained using general English text perform better. 

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5. What's in an ALT Tag? Exploring Caption Content Priorities through Collaborative Captioning

   https://doi.org/10.1145/3507659  

   Muehlbradt, Annika ; Kane, Shaun K. ( March 2022 , ACM Transactions on Accessible Computing)

   Evaluating the quality of accessible image captions with human raters is difficult, as it may be difficult for a visually impaired user to know how comprehensive a caption is, whereas a sighted assistant may not know what information a user will need from a caption. To explore how image captioners and caption consumers assess caption content, we conducted a series of collaborative captioning sessions in which six pairs, consisting of a blind person and their sighted partner, worked together to discuss, create, and evaluate image captions. By making captioning a collaborative task, we were able to observe captioning strategies, to elicit questions and answers about image captions, and to explore blind users’ caption preferences. Our findings provide insight about the process of creating good captions and serve as a case study for cross-ability collaboration between blind and sighted people. 

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