Improving accuracy, time spent, and ubiquity of delineation has been a long-standing design aim, yet many HCI works have overlooked high-stakes and complex healthcare annotation. We explore contouring, a critical workflow aimed at identifying and segmenting tumors, usually performed on immobile desktop computers in clinics, in which limited support for mobile access leads to prolonged and subpar treatment planning. Following interviews and think-aloud studies (N = 10 physicians), we report key contouring behaviors, and later design a novel cross-device prototype that enables contouring on everyday touch devices. We compared contouring via desktop and touch in a lab study (N = 8 residents) and found that mobile phones not only yielded similar accuracy, but also took significantly less time. Our results point to three broad design guidelines for cross-device solutions deployed within standalone healthcare workflows, and highlight how incorporating different device and input modalities can improve treatment delivery in today’s distributed healthcare environments.

Borui Wang, Chengcheng Feng, Arjun Nair, Madelyn Mao, Jai Desai, Asli Celikyilmaz, Haoran Li, Yashar Mehdad, Dragomir Radev. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022.

Liangke Gui, Borui Wang, Qiuyuan Huang, Alexander Hauptmann, Yonatan Bisk, Jianfeng Gao. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

Xiangru Tang, Alexander Fabbri, Haoran Li, Ziming Mao, Griffin Adams, Borui Wang, Asli Celikyilmaz, Yashar Mehdad, Dragomir Radev. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

Xiangru Tang, Arjun Nair, Borui Wang, Bingyao Wang, Jai Desai, Aaron Wade, Haoran Li, Asli Celikyilmaz, Yashar Mehdad, Dragomir Radev. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

Alexander Fabbri, Faiaz Rahman, Imad Rizvi, Borui Wang, Haoran Li, Yashar Mehdad, Dragomir Radev. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Modeling and prediction of human motion dynamics has long been a challenging problem in computer vision, and most existing methods rely on the end-to-end supervised training of various architectures of recurrent neural networks. Inspired by the recent success of deep reinforcement learning methods, in this paper we propose a new reinforcement learning formulation for the problem of human pose prediction, and develop an imitation learning algorithm for predicting future poses under this formulation through a combination of behavioral cloning and generative adversarial imitation learning. Our experiments show that our proposed method outperforms all existing state-of-the-art baseline models by large margins on the task of human pose prediction in both short-term predictions and long-term predictions, while also enjoying huge advantage in training speed.

Forecasting human dynamics is a very interesting but challenging task with several prospective applications in robotics, health-care, among others. Researchers have recently developed methods for human pose forecasting; but unfortunately, they often introduce a number of simplification assumptions. For instance, previous work either focuses only on short-term or long-term predictions, while sacrificing one or the other. Furthermore, they use the activity labels as part of the training process and require them to be available at testing time. These simplifications limit the usage of such pose forecasting models for real-world applications. To overcome these limitations, we propose a new action-agnostic method for short-and long-term human pose forecasting. Our triangular-prism recurrent neural network (TP-RNN) models the hierarchical and multi-scale characteristics of human dynamics. Our model captures the latent hierarchical structure in human pose sequences by encoding temporal dependencies with different time-scales. We run an extensive set of experiments on Human 3.6M and Penn Action datasets and show that our method outperforms baseline and state-of-the-art methods quantitatively and qualitatively. Code is available at https://github.com/eddyhkchiu/pose_forecast_wacv/.