ProtoX: Explaining a Reinforcement Learning Agent via Prototyping

While deep reinforcement learning has proven to be successful in solving control tasks, the "black-box" nature of an agent has received increasing concerns. We propose a prototype-based post-hoc policy explainer, ProtoX, that explains a blackbox agent by prototyping the agent's behaviors into scenarios, each represented by a prototypical state. When learning prototypes, ProtoX considers both visual similarity and scenario similarity. The latter is unique to the reinforcement learning context, since it explains why the same action is taken in visually different states. To teach ProtoX about visual similarity, we pre-train an encoder using contrastive learning via self-supervised learning to recognize states as similar if they occur close together in time and receive the same action from the black-box agent. We then add an isometry layer to allow ProtoX to adapt scenario similarity to the downstream task. ProtoX is trained via imitation learning using behavior cloning, and thus requires no access to the environment or agent. In addition to explanation fidelity, we design different prototype shaping terms in the objective function to encourage better interpretability. We conduct various experiments to test ProtoX. Results show that ProtoX achieved high fidelity to the original black-box agent while providing meaningful and understandable explanations

Dental anomaly detection using intraoral photos via deep learning

Children with orofacial clefting (OFC) present with a wide range of dental anomalies. Identifying these anomalies is vital to understand their etiology and to discern the complex phenotypic spectrum of OFC. Such anomalies are currently identified using intra-oral exams by dentists, a costly and time-consuming process. We claim that automating the process of anomaly detection using deep neural networks (DNNs) could increase efficiency and provide reliable anomaly detection while potentially increasing the speed of research discovery. This study characterizes the use of` DNNs to identify dental anomalies by training a DNN model using intraoral photographs from the largest international cohort to date of children with nonsyndromic OFC and controls (OFC1). In this project, the intraoral images were submitted to a Convolutional Neural Network model to perform multi-label multi-class classification of 10 dental anomalies. The network predicts whether an individual exhibits any of the 10 anomalies and can do so significantly faster than a human rater can. For all but three anomalies, F1 scores suggest that our model performs competitively at anomaly detection when compared to a dentist with 8 years of clinical experience. In addition, we use saliency maps to provide a post-hoc interpretation for our model’s predictions. This enables dentists to examine and verify our model’s predictions.Fil: Ragodos, Ronilo. University of Iowa; Estados UnidosFil: Wang, Tong. University of Iowa; Estados UnidosFil: Padilla, Carmencita. University of the Philippines; FilipinasFil: Hecht, Jacqueline T.. University of Texas Health Science Center at Houston; Estados UnidosFil: Poletta, Fernando Adrián. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. CEMIC-CONICET. Centro de Educaciones Médicas e Investigaciones Clínicas "Norberto Quirno". CEMIC-CONICET; ArgentinaFil: Orioli, Ieda Maria. Universidade Federal do Rio de Janeiro; BrasilFil: Buxó, Carmen J.. Universidad de Puerto Rico; Puerto RicoFil: Butali, Azeez. University of Iowa; Estados UnidosFil: Valencia Ramirez, Consuelo. Fundación Clínica Noel; ColombiaFil: Restrepo Muñeton, Claudia. Fundación Clínica Noel; ColombiaFil: Wehby, George. University of Iowa; Estados UnidosFil: Weinberg, Seth M.. University of Pittsburgh; Estados Unidos. University of Pittsburgh at Johnstown; Estados UnidosFil: Marazita, Mary L.. University of Pittsburgh at Johnstown; Estados Unidos. University of Pittsburgh; Estados UnidosFil: Moreno Uribe, Lina M.. University of Iowa; Estados UnidosFil: Howe, Brian J.. University of Iowa; Estados Unido

Author Correction: Dental anomaly detection using intraoral photos via deep learning (Scientific Reports, (2022), 12, 1, (11577), 10.1038/s41598-022-15788-1)

In the original version of this Article Ronilo Ragodos, Tong Wang and Brian J. Howe were omitted as equally contributing authors. Tong Wang was omitted as an additional corresponding author. Correspondence and requests for materials should also be addressed to [email protected]. In addition, the Author Contributions section in this Article was incorrect.Fil: Ragodos, Ronilo. University of Iowa; Estados UnidosFil: Wang, Tong. University of Iowa; Estados UnidosFil: Padilla, Carmencita. University of the Philippines; FilipinasFil: Hecht, Jacqueline T.. University of Texas; Estados UnidosFil: Poletta, Fernando Adrián. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. CEMIC-CONICET. Centro de Educaciones Médicas e Investigaciones Clínicas "Norberto Quirno". CEMIC-CONICET; ArgentinaFil: Orioli, Ieda Maria. Universidade Federal do Rio de Janeiro; BrasilFil: Buxó, Carmen J.. Universidad de Puerto Rico; Puerto RicoFil: Butali, Azeez. University of Iowa; Estados UnidosFil: Valencia Ramirez, Consuelo. Clinica Noel; ColombiaFil: Restrepo Muñeton, Claudia. Clinica Noel; ColombiaFil: Wehby, George. University of Iowa; Estados UnidosFil: Weinberg, Seth M.. University of Pittsburgh; Estados Unidos. University of Pittsburgh at Johnstown; Estados UnidosFil: Marazita, Mary L.. University of Pittsburgh at Johnstown; Estados Unidos. University of Pittsburgh; Estados UnidosFil: Moreno Uribe, Lina M.. University of Iowa; Estados UnidosFil: Howe, Brian J.. University of Iowa; Estados Unido