风险建模

The increasing prevalence of non-alcoholic fatty liver disease (NAFLD) has caused public concern in recent years. The high prevalence and risk of severe complications make monitoring NAFLD progression a public health priority. Fibrosis staging from liver biopsy images plays a key role in demonstrating the histological progression of NAFLD. Fibrosis mainly involves the deposition of fibers around vessels. Current deep learning-based fibrosis staging methods learn spatial relationships between tissue patches but do not explicitly consider the relationships between vessels and fibers leading to limited performance and poor interpretability. In this paper we propose an eXplicit vessel-fiber modeling method for Fibrosis staging from liver biopsy images namely XFibrosis. Specifically we transform vessels and fibers into graph-structured representations where their micro-structures are depicted by vessel-induced primal graphs and fiber-induced dual graphs respectively. Moreover the fiber-induced dual graphs also represent the connectivity information between vessels caused by fiber deposition. A primal-dual graph convolution module is designed to facilitate the learning of spatial relationships between vessels and fibers allowing for the joint exploration and interaction of their micro-structures. Experiments conducted on two datasets have shown that explicitly modeling the relationship between vessels and fibers leads to improved fibrosis staging and enhanced interpretability.

Deep Neural Networks (DNNs) are widely used for their ability to effectively approximate large classes of functions. This flexibility however makes the strict enforcement of constraints on DNNs a difficult problem. In contexts where it is critical to limit the function space to which certain network components belong such as wavelets employed in Multi-Resolution Analysis (MRA) naive constraints via additional terms in the loss function are inadequate. To address this we introduce a Convolutional Neural Network (CNN) wherein the convolutional filters are strictly constrained to be wavelets. This allows the filters to update to task-optimized wavelets during the training procedure. Our primary contribution lies in the rigorous formulation of these filters via a constrained empirical risk minimization framework thereby providing an exact mechanism to enforce these structural constraints. While our work is grounded in theory we investigate our approach empirically through applications in medical imaging particularly in the task of contour prediction around various organs achieving superior performance compared to baseline methods.

Model calibration measuring the alignment between the prediction accuracy and model confidence is an important metric reflecting model trustworthiness. Existing dense binary classification methods without proper regularisation of model confidence are prone to being over-confident. To calibrate Deep Neural Networks (DNNs) we propose a Self-Calibrating Vicinal Risk Minimisation (SCVRM) that explores the vicinity space of labeled data where vicinal images that are farther away from labeled images adopt the groundtruth label with decreasing label confidence. We prove that in the logistic regression problem SCVRM can be seen as a Vicinal Risk Minimisation plus a regularisation term that penalises the over-confident predictions. In practical implementation SCVRM is approximated using Monte Carlo sampling that samples additional augmented training images and labels from the vicinal distributions. Experimental results demonstrate that SCVRM can significantly enhance model calibration for different dense classification tasks on both in-distribution and out-of-distribution data. Code is available at https://github.com/Carlisle-Liu/SCVRM.

Machine learning holds tremendous promise for transforming the fundamental practice of scientific discovery by virtue of its data-driven nature. With the ever-increasing stream of research data collection it would be appealing to autonomously explore patterns and insights from observational data for discovering novel classes of phenotypes and concepts. However in the biomedical domain there are several challenges inherently presented in the cumulated data which hamper the progress of novel class discovery. The non-i.i.d. data distribution accompanied by the severe imbalance among different groups of classes essentially leads to ambiguous and biased semantic representations. In this work we present a geometry-constrained probabilistic modeling treatment to resolve the identified issues. First we propose to parameterize the approximated posterior of instance embedding as a marginal von Mises-Fisher distribution to account for the interference of distributional latent bias. Then we incorporate a suite of critical geometric properties to impose proper constraints on the layout of constructed embedding space which in turn minimizes the uncontrollable risk for unknown class learning and structuring. Furthermore a spectral graph-theoretic method is devised to estimate the number of potential novel classes. It inherits two intriguing merits compared to existent approaches namely high computational efficiency and flexibility for taxonomy-adaptive estimation. Extensive experiments across various biomedical scenarios substantiate the effectiveness and general applicability of our method.

Camera-parameter-free multi-view pose estimation is an emerging technique for 3D human pose estimation (HPE). They can infer the camera settings implicitly or explicitly to mitigate the depth uncertainty impact showcasing significant potential in real applications. However due to the limited camera setting diversity in the available datasets the inferred camera parameters are always simply hardcoded into the model during training and not adaptable to the input in inference making the learned models cannot generalize well under unseen camera settings. A natural solution is to artificially synthesize some samples i.e. 2D-3D pose pairs under massive new camera settings. Unfortunately to prevent over-fitting the existing camera setting the number of synthesized samples for each new camera setting should be comparable with that for the existing one which multiplies the scale of training and even makes it computationally prohibitive. In this paper we propose a novel HPE approach under the invariant risk minimization (IRM) paradigm. Precisely we first synthesize 2D poses from myriad camera settings. We then train our model under the IRM paradigm which targets at learning a common optimal model across all camera settings and thus enforces the model to automatically learn the camera parameters based on the input data. This allows the model to accurately infer 3D poses on unseen data by training on only a handful of samples from each synthesized setting and thus avoid the unbearable training cost increment. Another appealing feature of our method is that benefited from the capability of IRM in identifying the invariant features its performance on the seen camera settings is enhanced as well. Comprehensive experiments verify the superiority of our approach.