Conversion Between CT and MRI Images Using Diffusion and Score-Matching Models

MRI and CT are most widely used medical imaging modalities. It is often necessary to acquire multi-modality images for diagnosis and treatment such as radiotherapy planning. However, multi-modality imaging is not only costly but also introduces misalignment between MRI and CT images. To address this challenge, computational conversion is a viable approach between MRI and CT images, especially from MRI to CT images. In this paper, we propose to use an emerging deep learning framework called diffusion and score-matching models in this context. Specifically, we adapt denoising diffusion probabilistic and score-matching models, use four different sampling strategies, and compare their performance metrics with that using a convolutional neural network and a generative adversarial network model. Our results show that the diffusion and score-matching models generate better synthetic CT images than the CNN and GAN models. Furthermore, we investigate the uncertainties associated with the diffusion and score-matching networks using the Monte-Carlo method, and improve the results by averaging their Monte-Carlo outputs. Our study suggests that diffusion and score-matching models are powerful to generate high quality images conditioned on an image obtained using a complementary imaging modality, analytically rigorous with clear explainability, and highly competitive with CNNs and GANs for image synthesis

Low-Dose CT Using Denoising Diffusion Probabilistic Model for 20×\times Speedup

Low-dose computed tomography (LDCT) is an important topic in the field of radiology over the past decades. LDCT reduces ionizing radiation-induced patient health risks but it also results in a low signal-to-noise ratio (SNR) and a potential compromise in the diagnostic performance. In this paper, to improve the LDCT denoising performance, we introduce the conditional denoising diffusion probabilistic model (DDPM) and show encouraging results with a high computational efficiency. Specifically, given the high sampling cost of the original DDPM model, we adapt the fast ordinary differential equation (ODE) solver for a much-improved sampling efficiency. The experiments show that the accelerated DDPM can achieve 20x speedup without compromising image quality

Towards Faithful Model Explanation in NLP: A Survey

End-to-end neural NLP architectures are notoriously difficult to understand, which gives rise to numerous efforts towards model explainability in recent years. An essential principle of model explanation is Faithfulness, i.e., an explanation should accurately represent the reasoning process behind the model's prediction. This survey first discusses the definition and evaluation of Faithfulness, as well as its significance for explainability. We then introduce the recent advances in faithful explanation by grouping approaches into five categories: similarity methods, analysis of model-internal structures, backpropagation-based methods, counterfactual intervention, and self-explanatory models. Each category will be illustrated with its representative studies, advantages, and shortcomings. Finally, we discuss all the above methods in terms of their common virtues and limitations, and reflect on future work directions towards faithful explainability. For researchers interested in studying interpretability, this survey will offer an accessible and comprehensive overview of the area, laying the basis for further exploration. For users hoping to better understand their own models, this survey will be an introductory manual helping with choosing the most suitable explanation method(s).Comment: 62 page

Representation Of Lexical Stylistic Features In Language Models' Embedding Space

The representation space built by pretrained Language Models (LMs) encodes rich information about words and their relationships (e.g., similarity, hypernymy/hyponymy, polysemy) as well as abstract semantic notions (e.g., intensity). In this paper, we demonstrate that lexical stylistic notions such as complexity, formality, and figurativeness, can also be identified in this space. We show that it is possible to derive a vector representation for each of these stylistic notions, from only a small number of seed text pairs. Using these vectors, we can characterize new texts in terms of these dimensions using simple calculations in the corresponding embedding space. We perform experiments on five datasets and find that static embeddings encode these features more accurately at the level of words and phrases, whereas contextualized LMs perform better on longer texts. The lower performance of contextualized representations at the word level is partially attributable to the anisotropy of their vector space, which can be corrected through techniques like standardization to further improve performance.Comment: Accepted at *SEM 202