Smoke veil prior regularized surgical field desmoking without paired in-vivo data.

Though deep learning-based surgical smoke removal methods have shown significant improvements in effectiveness and efficiency, the lack of paired smoke and smoke-free images in real surgical scenarios limits the performance of these methods. Therefore, methods that can achieve good generalization performance without paired in-vivo data are in high demand. In this work, we propose a smoke veil prior regularized two-stage smoke removal framework based on the physical model of smoke image formation. More precisely, in the first stage, we leverage a reconstruction loss, a consistency loss and a smoke veil prior-based regularization term to perform fully supervised training on a synthetic paired image dataset. Then a self-supervised training stage is deployed on the real smoke images, where only the consistency loss and the smoke veil prior-based loss are minimized. Experiments show that the proposed method outperforms the state-of-the-art ones on synthetic dataset. The average PSNR, SSIM and RMSE values are 21.99±2.34, 0.9001±0.0252 and 0.2151±0.0643, respectively. The qualitative visual inspection on real dataset further demonstrates the effectiveness of the proposed method.

Federated Partially Supervised Learning With Limited Decentralized Medical Images.

Data government has played an instrumental role in securing the privacy-critical infrastructure in the medical domain and has led to an increased need of federated learning (FL). While decentralization can limit the effectiveness of standard supervised learning, the impact of decentralization on partially supervised learning remains unclear. Besides, due to data scarcity, each client may have access to only limited partially labeled data. As a remedy, this work formulates and discusses a new learning problem federated partially supervised learning (FPSL) for limited decentralized medical images with partial labels. We study the impact of decentralized partially labeled data on deep learning-based models via an exemplar of FPSL, namely, federated partially supervised learning multi-label classification. By dissecting FedAVG, a seminal FL framework, we formulate and analyze two major challenges of FPSL and propose a simple yet robust FPSL framework, FedPSL, which addresses these challenges. In particular, FedPSL contains two modules, task-dependent model aggregation and task-agnostic decoupling learning, where the first module addresses the weight assignment and the second module improves the generalization ability of the feature extractor. We provide a comprehensive empirical understanding of FSPL under data scarcity with simulated experiments. The empirical results not only indicate that FPSL is an under-explored problem with practical value but also show that the proposed FedPSL can achieve robust performance against baseline methods on data challenges such as data scarcity and domain shifts. The findings of this study also pose a new research direction towards label-efficient learning on medical images.

ConnNet: A Long-Range Relation-Aware Pixel-Connectivity Network for Salient Segmentation.

Salient segmentation aims to segment out attentiongrabbing regions, a critical yet challenging task and the foundation of many high-level computer vision applications. It requires semantic-aware grouping of pixels into salient regions and benefits from the utilization of global multi-scale contexts to achieve good local reasoning. Previous works often address it as two-class segmentation problems utilizing complicated multi-step procedures including refinement networks and complex graphical models. We argue that semantic salient segmentation can instead be effectively resolved by reformulating it as a simple yet intuitive pixel-pair based connectivity prediction task. Following the intuition that salient objects can be naturally grouped via semanticaware connectivity between neighboring pixels, we propose a pure Connectivity Net (ConnNet). ConnNet predicts connectivity probabilities of each pixel with its neighboring pixels by leveraging multi-level cascade contexts embedded in the image and long-range pixel relations. We investigate our approach on two tasks, namely salient object segmentation and salient instancelevel segmentation, and illustrate that consistent improvements can be obtained by modeling these tasks as connectivity instead of binary segmentation tasks for a variety of network architectures. We achieve state-of-the-art performance, outperforming or being comparable to existing approaches while reducing inference time due to our less complex approach.