Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-Aided Diagnosis
By Hyebin Lee, Seong Tae Kim, Yong Man Ro. 2019
44 citations recorded by the source corpus.
The ambiguity of the decision-making process has been pointed out as the main obstacle to applying the deep learning-based method in a practical way in spite of its outstanding performance. Interpretability could guarantee the confidence of deep learning system, therefore it is particularly important in the medical field. In this study, a novel deep network is proposed to explain the diagnostic decision with visual pointing map and diagnostic sentence justifying result simultaneously. For the purpose of increasing the accuracy of sentence generation, a visual word constraint model is devised in training justification generator. To verify the proposed method, comparative experiments were conducted on the problem of the diagnosis of breast masses. Experimental results demonstrated that the proposed deep network could explain diagnosis more accurately with various textual justifications.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Amitojdeep Singh
- Cuntai Guan
- Max A. Viergever
- Hugo J. Kuijf
- Kenneth G. A. Gilhuijs
- Bas H. M. van der Velden
- Vasudevan Lakshminarayanan
- Erico Tjoa
In Computer Vision and Pattern Recognition
- Amitojdeep Singh
- Cuntai Guan
- Max A. Viergever
- Hugo J. Kuijf
- Kenneth G. A. Gilhuijs
- Bas H. M. van der Velden
- Vasudevan Lakshminarayanan
- Erico Tjoa
Citations in the corpus
- A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI (2019, background): “In the medical field (see later section), [6], [43], [63]–[69] have studied methods employing saliency and visual explanations.”
- Explainable Deep Learning Models in Medical Image Analysis (2020, methodology): “Monotonic constraints [63] DNN with two streams Melanoma detection Dermoscopy images Textual justification LSTM, visual word constraint [64] Breast mass classification CNN Mammography images”
- Explainable artificial intelligence (XAI) in deep learning-based medical image analysis (2021, background): “Li et al. (2019) MRI Examples from the latent space Breast Uehara et al.”