PnPNet: Pull-and-Push Networks for Volumetric Segmentation with Boundary Confusion

Precise boundary segmentation of volumetric images is a critical task for image-guided diagnosis and computer-assisted intervention, especially for boundary confusion in clinical practice. However, U-shape networks cannot effectively resolve this challenge due to the lack of boundary shape constraints. Besides, existing methods of refining boundaries overemphasize the slender structure, which results in the overfitting phenomenon due to networks' limited abilities to model tiny objects. In this paper, we reconceptualize the mechanism of boundary generation by encompassing the interaction dynamics with adjacent regions. Moreover, we propose a unified network termed PnPNet to model shape characteristics of the confused boundary region. Core ingredients of PnPNet contain the pushing and pulling branches. Specifically, based on diffusion theory, we devise the semantic difference module (SDM) from the pushing branch to squeeze the boundary region. Explicit and implicit differential information inside SDM significantly boost representation abilities for inter-class boundaries. Additionally, motivated by the K-means algorithm, the class clustering module (CCM) from the pulling branch is introduced to stretch the intersected boundary region. Thus, pushing and pulling branches will shrink and enlarge the boundary uncertainty respectively. They furnish two adversarial forces to promote models to output a more precise delineation of boundaries. We carry out experiments on three challenging public datasets and one in-house dataset, containing three types of boundary confusion in model predictions. Experimental results demonstrate the superiority of PnPNet over other segmentation networks, especially on evaluation metrics of HD and ASSD. Besides, pushing and pulling branches can serve as plug-and-play modules to enhance classic U-shape baseline models. Codes are available.

Implicit Shape Modeling for Anatomical Structure Refinement of Volumetric Medical Images

Shape modeling of volumetric medical images is a critical task for quantitative analysis and surgical plans in computer-aided diagnosis. To relieve the burden of expert clinicians, the reconstructed shapes are widely acquired from deep learning models, e.g. Convolutional Neural Networks (CNNs), followed by marching cube algorithm. However, automatically obtaining reconstructed shapes can not always achieve perfect results due to the limited resolution of images and lack of shape prior constraints. In this paper, we design a unified framework for the refinement of medical image segmentation on top of an implicit neural network. Specifically, To learn a sharable shape prior from different instances within the same category in the training phase, the physical information of volumetric medical images are firstly utilized to construct the Physical-Informed Continuous Coordinate Transform (PICCT). PICCT transforms the input data in an aligned manner fed into the implicit shape modeling. To better learn shape representation, we introduce implicit shape constraints on top of the signed distance function (SDF) into the implicit shape modeling of both instances and latent template. For the inference phase, a template interaction module (TIM) is proposed to refine initial results produced by CNNs via deforming deep implicit templates with latent codes. Experimental results on three datasets demonstrated the superiority of our approach in shape refinement. The Chamfer Distance/Earth Mover's Distance achieved by the proposed method are 0.232/0.087 on the Liver dataset, 0.128/0.069 on the Pancreas dataset, and 0.417/0.100 on the Lung Lobe dataset.

Learning with Explicit Shape Priors for Medical Image Segmentation

Medical image segmentation is considered as the basic step for medical image analysis and surgical intervention. And many previous works attempted to incorporate shape priors for designing segmentation models, which is beneficial to attain finer masks with anatomical shape information. Here in our work, we detailedly discuss three types of segmentation models with shape priors, which consist of atlas-based models, statistical-based models and UNet-based models. On the ground that the former two kinds of methods show a poor generalization ability, UNet-based models have dominated the field of medical image segmentation in recent years. However, existing UNet-based models tend to employ implicit shape priors, which do not have a good interpretability and generalization ability on different organs with distinctive shapes. Thus, we proposed a novel shape prior module (SPM), which could explicitly introduce shape priors to promote the segmentation performance of UNet-based models. To evaluate the effectiveness of SPM, we conduct experiments on three challenging public datasets. And our proposed model achieves state-of-the-art performance. Furthermore, SPM shows an outstanding generalization ability on different classic convolution-neural-networks (CNNs) and recent Transformer-based backbones, which can serve as a plug-and-play structure for the segmentation task of different datasets.

The Deep Learning Compiler: A Comprehensive Survey

The difficulty of deploying various deep learning (DL) models on diverse DL hardware has boosted the research and development of DL compilers in the community. Several DL compilers have been proposed from both industry and academia such as Tensorflow XLA and TVM. Similarly, the DL compilers take the DL models described in different DL frameworks as input, and then generate optimized codes for diverse DL hardware as output. However, none of the existing survey has analyzed the unique design of the DL compilers comprehensively. In this paper, we perform a comprehensive survey of existing DL compilers by dissecting the commonly adopted design in details, with emphasis on the DL oriented multi-level IRs, and frontend/backend optimizations. Specifically, we provide a comprehensive comparison among existing DL compilers from various aspects. In addition, we present detailed analysis of the multi-level IR design and compiler optimization techniques. Finally, several insights are highlighted as the potential research directions of DL compiler. This is the first survey paper focusing on the unique design of DL compiler, which we hope can pave the road for future research towards the DL compiler.

swCaffe: a Parallel Framework for Accelerating Deep Learning Applications on Sunway TaihuLight

This paper reports our efforts on swCaffe, a highly efficient parallel framework for accelerating deep neural networks (DNNs) training on Sunway TaihuLight, the current fastest supercomputer in the world that adopts a unique many-core heterogeneous architecture, with 40,960 SW26010 processors connected through a customized communication network. First, we point out some insightful principles to fully exploit the performance of the innovative many-core architecture. Second, we propose a set of optimization strategies for redesigning a variety of neural network layers based on Caffe. Third, we put forward a topology-aware parameter synchronization scheme to scale the synchronous Stochastic Gradient Descent (SGD) method to multiple processors efficiently. We evaluate our framework by training a variety of widely used neural networks with the ImageNet dataset. On a single node, swCaffe can achieve 23\%\~{}119\% overall performance compared with Caffe running on K40m GPU. As compared with the Caffe on CPU, swCaffe runs 3.04\~{}7.84x faster on all the networks. Finally, we present the scalability of swCaffe for the training of ResNet-50 and AlexNet on the scale of 1024 nodes.