pwc-archive/papers-with-abstracts huggingface dataset:
Multi-magnification networks for deformable image registration on histopathology images
Authors: Anonymous
Date: 2022-02-04 00:00:00
Proceeding: WBIR Workshop Biomedical_Imaging_Registration 2022 7
Tasks: Image Registration, whole slide images

We present an end-to-end unsupervised deformable registration approach for high-resolution histopathology images with different stains. Our method comprises two sequential registration networks, where the local affine network can handle small deformations, and the non-rigid network is able to align texture details further. Both networks adopt the multi-magnification structure to improve registration accuracy. We train the proposed networks separately and evaluate them on the dataset provided by the University Hospital Frankfurt, which contains 41 multi-stained histopathology whole-slide images. By comparing with methods using the single-magnification structure, we confirm that the proposed multi-view architecture can significantly improve the performance of the local affine registration algorithm. Moreover, the proposed method achieves high registration accuracy of contents at the cell level and is potentially applicable to other medical image alignment tasks. A PyTorch implementation of the proposed algorithm is available at {https://anonymous.4open.science/r/MMDReg-27A2/README.md}.
Cross-Sim-NGF: FFT-Based Global Rigid Multimodal Alignment of Image Volumes using Normalized Gradient Fields
Authors: Anonymous
Date: 2022-02-04 00:00:00
Proceeding: WBIR Workshop Biomedical_Imaging_Registration 2022 7

Multimodal image alignment involves finding spatial correspondences between volumes varying in appearance and structure. Automated alignment methods are often based on local optimization that can be highly sensitive to initialization. We propose a novel efficient algorithm for computing similarity of normalized gradient fields (NGF) in the frequency domain, which we globally optimize to achieve rigid multimodal 3D image alignment. We validate the method experimentally on a dataset comprised of 20 brain volumes acquired in four modalities (T1w, Flair, CT, [18F] FDG PET), synthetically displaced with known transformations. The proposed method exhibits excellent performance on all six possible modality combinations and outperforms the four considered reference methods by a large margin. An important advantage of the method is its speed; global rigid alignment of 3.4\,Mvoxel volumes requires approximately 40 seconds of computation, and the proposed algorithm outperforms a direct algorithm for the same task by more than three orders of magnitude. Open-source code is provided.
A multi-organ point cloud registration algorithm for abdominal CT registration
Authors: Anonymous
Date: 2022-02-04 00:00:00
Proceeding: WBIR Workshop Biomedical_Imaging_Registration 2022 7
Tasks: Point Cloud Registration

Registering CT images of the chest is a crucial step for several tasks such as disease progression tracking or surgical planning. It is also a challenging step because of the heterogeneous content of the human abdomen which implies complex deformations. In this work, we focus on accurately registering a subset of organs of interest. We register organ surface point clouds, as may typically be extracted from an automatic segmentation pipeline, by expanding the Bayesian Coherent Point Drift algorithm (BCPD). 
We introduce MO-BCPD, a multi-organ version of the BCPD algorithm which explicitly models three important aspects of this task: organ individual elastic properties, inter-organ motion coherence and segmentation inaccuracy. This model also provides an interpolation framework to estimate the deformation of the entire volume. We demonstrate the efficiency of our method by registering different patients from the LITS challenge dataset. The target registration error on anatomical landmarks is almost twice as small for MO-BCPD compared to standard BCPD while imposing the same constraints on individual organs deformation.
Non-parametric Uncertainty Assessment in Deep-Learning Based Affine Image Registration
Authors: Anonymous
Date: 2022-02-04 00:00:00
Proceeding: WBIR Workshop Biomedical_Imaging_Registration 2022 7
Tasks: Affine Image Registration, Deep Learning, Image Registration, Uncertainty Quantification

Affine image registration plays a key role in diagnosis, surgical planning and in data-processing pipelines for research as both an essential initialization for subsequent non-rigid registration or as an independent step. Uncertainty quantification in deep learning (DL) based image registration models is critical for determining confidence intervals required for surgical guidance, and for reliable assessment of differences between the registered images. 
We introduce AIR-SGLD - a non-parametric fully Bayesian framework for Affine image registration. We use Stochastic gradient Langevin dynamics (SGLD) during the training phase to characterize the posterior distribution of the network weights. 
We demonstrated the added-value of AIR-SGLD on the brain MRI (MGH10) dataset in comparison to the baseline AIR DL-based Affine image registration framework using 300 pairs of images generated from the MGH10 dataset. Our experiments show that AIR-SGLD outperforms AIR by means of cross-correlation between the images ($0.91$ vs. $0.87$, $p<0.01$). Further, AIR-SGLD provides an estimate of the registration uncertainty that correlates with both registration error (Pearson correlation coefficient of $R=0.769$) and the presence of out-of-distribution data ($R=0.796$).
AIR-SGLD has the potential to provide reliable and more accurate registration for clinical diagnosis, surgical planning, and automatic data processing pipelines.
Deformable Image Registration uncertainty quantification using deep learning for dose accumulation in adaptive proton therapy
Authors: Anonymous
Date: 2022-02-04 00:00:00
Proceeding: WBIR Workshop Biomedical_Imaging_Registration 2022 7
Tasks: Anatomy, Image Registration, Uncertainty Quantification

Deformable image registration (DIR) is a key element in adaptive radiotherapy (AR) to include anatomical modifications in the adaptive planning. In AR, daily 3D images are acquired and DIR can be used for structure propagation and to deform the daily dose to a reference anatomy. Quantifying the uncertainty associated with DIR is essential. Here, a probabilistic unsupervised deep learning method is presented to predict the variance of a given deformable vector field (DVF). It is shown that the proposed method can predict the uncertainty associated with various conventional DIR algorithms for breathing deformation in the lung. In addition, we show that the uncertainty prediction is accurate also for DIR algorithms not used during the training. Finally, we demonstrate how the resulting DVFs can be used to estimate the dosimetric uncertainty arising from dose deformation.
A method for image registration via broken geodesics
Authors: Anonymous
Date: 2022-02-04 00:00:00
Proceeding: WBIR Workshop Biomedical_Imaging_Registration 2022 7
Tasks: Image Registration

Anatomical variabilities seen in longitudinal data or inter-subject data is usually described by the underlying deformation, captured by non-rigid registration of these images. Stationary Velocity Field (SVF) based non-rigid registration algorithms are widely used for registration. However, these methods cover only a limited degree of deformations. We address this limitation and define an approximate metric space for the manifold of diffeomorphisms $\mathcal{G}$. We propose a method to break down the large deformation into finite set of small sequential deformations. This results in a broken geodesic path on $\mathcal{G}$ and its length now forms an approximate registration metric. We illustrate the method using a simple, intensity-based, log-demon implementation. Validation results of the proposed method show that it can capture large and complex deformations while producing qualitatively better results than state-of-the-art methods. The results also demonstrate that the proposed registration metric is a good indicator of the degree of deformation.
Locally Normalized Gradient Fields for Multi-Modal Groupwise Image Registration
Authors: Anonymous
Date: 2022-02-04 00:00:00
Proceeding: WBIR Workshop Biomedical_Imaging_Registration 2022 7
Tasks: Image Registration

The Normalized Gradient Fields (NGF) similarity measure has shown its dominance for registering pairs of images from numerous modalities. Recently, gradient fields-based distance measures for groupwise image registration have been proposed, but these works have shown its usefulness only for mono-modal applications or dynamic imaging from the same modality. In this paper, we propose the groupwise Normalized Gradient Fields (gNGF) similarity measure for groupwise multi-modal image registration.

The normalization of gradient fields is one of the key components of the NGF measure where the normalization may be computed locally or globally. In this work, we motivate the local normalization for multi-modal registration where the correlations between gradient fields are maximized at every point in the spatial domain for registering images. We demonstrate the effectiveness and applicability to real-world data of our proposal through experiments on images from two datasets.
The results show that gNGF may be a long missing extension of NGF for multi-modal, multi-image applications.
Identifying Partial Mouse Brain Microscopy Images from the Allen Reference Atlas using a Contrastively Learned Semantic Space
Authors: Anonymous
Date: 2022-02-04 00:00:00
Proceeding: WBIR Workshop Biomedical_Imaging_Registration 2022 7

Registering mouse brain microscopy images to a reference atlas can help determine the locations of anatomical structures in the brain, which is an essential step for understanding the function of brain circuits. Most existing registration pipelines assume the identity of the reference plate -- to which the image slice is to be registered -- is known beforehand. This might not always be the case due to three main challenges in microscopy image data: missing image regions (partial data), different cutting angles compared to the atlas plates and a large number of high-resolution images to be identified. Manual identification of reference plates as an initial step can be biased, tedious and resource intensive. On the other hand, registering images to all atlas plates can be slow, limiting the application of automated registration methods when dealing with high-resolution image data. This work proposes to perform the image identification by learning a low-dimensional space that captures the similarity between microscopy images and the reference atlas plates. We employ Convolutional Neural Networks (CNNs), in the Siamese network configuration, to first obtain low-dimensional embeddings of microscopy image data and atlas plates. These embeddings are contrasted with positive and negative examples in order to learn a semantically meaningful space that can be used for identifying corresponding 2D atlas plates. At inference, atlas plates that are closest to the microscopy image data in the learned embedding space are presented as candidates for registration. Our method achieved TOP-3 and TOP-5 accuracy of 83.3% and 100%, respectively, compared to the SimpleElastix-based baseline which obtained 25% in both the Top-3 and Top-5 accuracy.
Voxelmorph++ Going beyond the cranial vault with keypoint supervision and multi-channel instance optimisation
Authors: Anonymous
Date: 2022-02-04 00:00:00
Proceeding: WBIR Workshop Biomedical_Imaging_Registration 2022 7
Tasks: Image Registration

The majority of current research in deep learning based image registration addresses inter-patient brain registration with moderate deformation magnitudes. The recent Learn2Reg medical registration benchmark has demonstrated that single-scale U-Net architectures, such as VoxelMorph that directly employ a spatial transformer loss, often do not generalise well beyond the cranial vault and fall short of state-of-the-art performance for abdominal or intra-patient lung registration. Here, we propose two straightforward steps that greatly reduce this gap in accuracy. First, we employ keypoint self-supervision with a novel network head that predicts a discretised heatmap and robustly reduces large deformations for better robustness. Second, we replace multiple learned fine-tuning steps by a single instance optimisation with hand-crafted features and the Adam optimiser. Different to other related work, including FlowNet or PDD-Net, our approach does not require a fully discretised architecture with correlation layer. Our ablation study demonstrates the importance of keypoints in both self-supervised and unsupervised (using only a MIND metric) settings. On a multi-centric inspiration-exhale lung CT dataset, including very challenging COPD scans, our method outperforms VoxelMorph by improving nonlinear alignment by 77% compared to 19% - reaching target registration errors of 2 mm that outperform all but one learning methods published to date. Extending the method to semantic features sets new stat-of-the-art performance on inter-subject abdominal CT registration.
DeepSTAPLE: Learning to predict multimodal registration quality for unsupervised domain adaptation
Authors: Anonymous
Date: 2022-02-04 00:00:00
Proceeding: WBIR Workshop Biomedical_Imaging_Registration 2022 7
Tasks: Domain Adaptation, Image Registration, Segmentation, Semantic Segmentation, Unsupervised Domain Adaptation

While deep neural networks often achieve outstanding results on semantic segmentation tasks within a dataset domain, performance can drop significantly when predicting domain-shifted input data. Multi-atlas segmentation utilizes multiple available sample annotations which are deformed and propagated to the target domain via multimodal image registration and fused to a consensus label afterwards but subsequent network training with the registered data may not yield optimal results due to registration errors. In this work, we propose to extend a curriculum learning approach with additional regularization and fixed weighting to train a semantic segmentation model along with data parameters representing the atlas confidence. Using these adjustments we can show that
registration quality information can be extracted out of a semantic segmentation model and further be used to create label consensi when using a straightforward weighting scheme. 
Comparing our results to the STAPLE method, we find that our consensi are not only a better approximation of the oracle-label regarding Dice score but also improve subsequent network training results.
LDDMM meets GANs: Generative Adversarial Networks for diffeomorphic registration
Authors: Anonymous
Date: 2022-02-04 00:00:00
Proceeding: WBIR Workshop Biomedical_Imaging_Registration 2022 7
Tasks: Image Registration

The purpose of this work is to contribute to the state of the art of deep-learning methods for diffeomorphic registration. We propose an adversarial learning LDDMM method for pairs of 3D mono-modal images based on Generative Adversarial Networks. The method is inspired by the recent literature for deformable image registration with adversarial learning. We combine the best performing generative, discriminative, and adversarial ingredients from the state of the art within the LDDMM paradigm. We have successfully implemented two models with the stationary and the EPDiff-constrained non-stationary parameterizations of diffeomorphisms. Our unsupervised learning approach has shown competitive performance with respect to benchmark supervised learning and model-based methods.
A Character-level Ngram-based MT Approach for Lexical Normalization in Social Media
Authors: Anonymous
Date: 2021-12-17 00:00:00
Proceeding: ACL ARR December 2022 12
Tasks: Lexical Normalization

This paper presents an ngram-based MT approach that operates at character-level to generate possible canonical forms for lexical variants in social media text. It utilizes a joint n-gram model to learn edit sequences of word pairs, thus overcomes the shortage of phrase-based approach that is unable to capture dependencies across phrases. We evaluate our approach on two English tweet datasets and observe that the ngram-based approach significantly outperforms phrase-based approach in normalization task. Our simple model achieves a broad coverage on diverse variants which is comparable to complex hybrid systems.
Deep Reinforcement Learning-based Authentic Dialogue Generation To Protect Youth From Cybergrooming
Authors: Anonymous
Date: 2021-12-17 00:00:00
Proceeding: ACL ARR December 2022 12
Tasks: Chatbot, Deep Reinforcement Learning, Dialogue Generation, reinforcement-learning, Reinforcement Learning (RL)

Cybergrooming is defined as a crime towards potential victims, especially teens, by building close personal relationships with them with the purpose of sexual exploitation via online media. Cyber or online sexual grooming has been recognized as a serious cyber crime. However, there have been insufficient programs to proactively protect the youth from cybergrooming.  In this work, we present a generative chatbot framework, called SERI (Stop cybERgroomIng), that can generate simulated conversations between a perpetrator chatbot and a potential victim chatbot.  To realize the simulation of authentic conversations in the context of cybergrooming, we take deep reinforcement learning (DRL)-based dialogue generation for authentic simulation of the conversations between a potential victim and a perpetrator (i.e., cybergroomer).  The design of the SERI is motivated to ensure a safe and authentic environment to strengthen the youth's precautionary awareness of cybergrooming while any unnecessary ethical issues (e.g., the potential misuse of the SERI) are removed or minimized.  We developed the SERI as a preliminary platform that can deploy the perpetrator chatbot to interact with human users (i.e., youth) to observe youth users' responses to strangers or acquaintances and collect the reactions when the youth users are asked for private or sensitive information by the perpetrator. We evaluated the quality of conversations generated by the SERI based on open-source, referenced, unreferenced metrics, and human evaluation.
Hybrid-Regressive Neural Machine Translation
Authors: Anonymous
Date: 2021-12-17 00:00:00
Proceeding: ACL ARR December 2022 12
Tasks: CPU, Machine Translation, Translation

Non-autoregressive translation (NAT) with iterative refinement mechanism has shown comparable performance with the auto-regressive counterpart. However, we have empirically found that decoding acceleration is fragile when using a large batch size and running on the CPU. We demonstrate that one-pass NAT is sufficient when providing a few target contexts in advance through synthetic experiments. Inspired by this, we propose a two-stage translation prototype -- Hybrid-Regressive Translation (HRT) to combine the strengths of autoregressive and non-autoregressive. Specifically, HRT first generates a discontinuous sequence by autoregression (e.g., make a prediction every k tokens, k>1) and then fills all previously skipped tokens at once in a non-autoregressive manner. We also propose a bag of techniques to effectively and efficiently train HRT, with almost no increase in parameters. Experimental results on WMT En-Ro, En-De, and NIST Zh-En show that our model outperforms existing semi-autoregressive models and is competitive with current state-of-the-art non-autoregressive models. Moreover, compared to its autoregressive counterpart, HRT has a stable 1.5x acceleration, regardless of batch size and device.
When is BERT Multilingual? Isolating Crucial Ingredients for Cross-lingual Transfer
Authors: Anonymous
Date: 2021-12-17 00:00:00
Proceeding: ACL ARR December 2022 12
Tasks: Cross-Lingual Transfer

While recent work on multilingual language models has demonstrated their capacity for cross-lingual zero-shot transfer on downstream tasks, there is a lack of consensus in the community as to what shared properties between languages enable such transfer. Analyses involving pairs of natural languages are often inconclusive and contradictory since languages simultaneously differ in many linguistic aspects. In this paper, we perform a large-scale empirical study to isolate the effects of various linguistic properties by measuring zero-shot transfer between four diverse natural languages and their counterparts constructed by modifying aspects such as the script, word order, and syntax. Among other things, our experiments show that the absence of sub-word overlap significantly affects zero-shot transfer when languages differ in their word order, and there is a strong correlation between transfer performance and word embedding alignment between languages (e.g., $\rho_s=0.94$ on the task of NLI). Our results call for focus in multilingual models on explicitly improving word embedding alignment between languages rather than relying on its implicit emergence.
Unsupervised Compressive Text Summarisation with Reinforcement Learning
Authors: Anonymous
Date: 2021-12-17 00:00:00
Proceeding: ACL ARR December 2022 12
Tasks: Hallucination, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recently, compressive text summarisation offers a balance between the conciseness issue of extractive summarisation and the factual hallucination issue of abstractive summarisation. However, most existing compressive summarisation methods are supervised, relying on the expensive effort of creating a new training dataset with corresponding compressive summaries. In this paper, we propose an unsupervised compressive summarisation method that utilises reinforcement learning to optimise a summary's semantic coverage and fluency by simulating human judgment on summarisation quality. Our model consists of an extractor agent and a compressor agent, and both agents have a multi-head attentional pointer-based structure. The extractor agent first chooses salient sentences from a document, and then the compressor agent compresses these extracted sentences by selecting salient words to form a summary without using reference summaries to compute the summary reward. That is, a parallel dataset with document-summary pairs is not required to train the proposed model. To the best of our knowledge, our proposed method is the first work on unsupervised compressive summarisation. Experimental results on three widely used datasets, Newsroom, CNN/DM, and XSum, show that our model achieves promising performance and significant improvement on Newsroom in terms of the ROUGE metric.
Iterative Decoding for Compositional Generalization in Transformers
Authors: Anonymous
Date: 2021-12-17 00:00:00
Proceeding: ACL ARR December 2022 12

Deep learning models generalize well to in-distribution data but struggle to generalize compositionally, i.e., to combine a set of learned primitives to solve more complex tasks. In sequence-to-sequence (seq2seq) learning, transformers are often unable to predict correct outputs for longer examples than those seen at training. This paper introduces iterative decoding, an alternative to seq2seq that (i) improves transformer compositional generalization in the PCFG and Cartesian product datasets and (ii) evidences that, in these datasets, seq2seq transformers do not learn iterations that are not unrolled. In iterative decoding, training examples are broken down into a sequence of intermediate steps that the transformer learns iteratively. At inference time, the intermediate outputs are fed back to the transformer as intermediate inputs until an end-of-iteration token is predicted. We conclude by illustrating some limitations of iterative decoding in the CFQ dataset.