JL
J. Luu
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Statistical distribution alignment methods for domain adaptation assume similar class distributions across domains, but this assumption cannot always be guaranteed in medical imaging data. This research investigates the effect of cross-domain class imbalance on statistical distribution alignment in unsupervised domain adaptation for medical image classification. Our experiments demonstrate that statistical distribution alignment using MMD performs reliably under mild domain shifts but struggles when both severe cross-domain class imbalance and complex domain shifts are present. To address this, we implement class-conditioned domain alignment with a new weighted minibatch sampling method. Under conditions of extreme domain shift and severe cross-domain class imbalance, combining statistical distribution alignment with more complex sampling strategies results in small improvements compared to alignment with random sampling, suggesting that class-conditioned distribution alignment offers limited practical benefits. The model appears robust to label noise, but since the performance gains are tiny, the choice of sampling strategy could have limited influence on overall performance. In our experiments, we employ the CHECK and OAI hip X-ray datasets to investigate binary osteoarthritis classification under varying levels of domain shift and cross-domain class imbalance.
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Statistical distribution alignment methods for domain adaptation assume similar class distributions across domains, but this assumption cannot always be guaranteed in medical imaging data. This research investigates the effect of cross-domain class imbalance on statistical distribution alignment in unsupervised domain adaptation for medical image classification. Our experiments demonstrate that statistical distribution alignment using MMD performs reliably under mild domain shifts but struggles when both severe cross-domain class imbalance and complex domain shifts are present. To address this, we implement class-conditioned domain alignment with a new weighted minibatch sampling method. Under conditions of extreme domain shift and severe cross-domain class imbalance, combining statistical distribution alignment with more complex sampling strategies results in small improvements compared to alignment with random sampling, suggesting that class-conditioned distribution alignment offers limited practical benefits. The model appears robust to label noise, but since the performance gains are tiny, the choice of sampling strategy could have limited influence on overall performance. In our experiments, we employ the CHECK and OAI hip X-ray datasets to investigate binary osteoarthritis classification under varying levels of domain shift and cross-domain class imbalance.
This research experiment aimed to investigate the level of trust placed in an AI negotiation assistant paired with a truthful explanation of their negotiation strategy versus an opposite explanation within the Pocket Negotiator platform. A between-user study involving 30 participants was conducted to assess participants’ trust perceptions based on the presentation of different explanations about the negotiation assistant. After receiving an explanation
of the negotiation strategy used by the assistant, participants went through a bilateral negotiation on the Pocket Negotiator platform against a robot after
which they completed a questionnaire to evaluate their trust in the assistant. The results were insignificant (p > 0.05) and therefore no conclusion could be drawn about the difference in the participants’ trust in the assistant with a truthful explanation and the assistant with an opposite explanation. ...
of the negotiation strategy used by the assistant, participants went through a bilateral negotiation on the Pocket Negotiator platform against a robot after
which they completed a questionnaire to evaluate their trust in the assistant. The results were insignificant (p > 0.05) and therefore no conclusion could be drawn about the difference in the participants’ trust in the assistant with a truthful explanation and the assistant with an opposite explanation. ...
This research experiment aimed to investigate the level of trust placed in an AI negotiation assistant paired with a truthful explanation of their negotiation strategy versus an opposite explanation within the Pocket Negotiator platform. A between-user study involving 30 participants was conducted to assess participants’ trust perceptions based on the presentation of different explanations about the negotiation assistant. After receiving an explanation
of the negotiation strategy used by the assistant, participants went through a bilateral negotiation on the Pocket Negotiator platform against a robot after
which they completed a questionnaire to evaluate their trust in the assistant. The results were insignificant (p > 0.05) and therefore no conclusion could be drawn about the difference in the participants’ trust in the assistant with a truthful explanation and the assistant with an opposite explanation.
of the negotiation strategy used by the assistant, participants went through a bilateral negotiation on the Pocket Negotiator platform against a robot after
which they completed a questionnaire to evaluate their trust in the assistant. The results were insignificant (p > 0.05) and therefore no conclusion could be drawn about the difference in the participants’ trust in the assistant with a truthful explanation and the assistant with an opposite explanation.