Pick the Best Pre-trained Model: Towards Transferability Estimation for Medical Image Segmentation

Transfer learning is a critical technique in training deep neural networks for the challenging medical image segmentation task that requires enormous resources. With the abundance of medical image data, many research institutions release models trained on various datasets that can form a huge pool of candidate source models to choose from. Hence, it's vital to estimate the source models' transferability (i.e., the ability to generalize across different downstream tasks) for proper and efficient model reuse. To make up for its deficiency when applying transfer learning to medical image segmentation, in this paper, we therefore propose a new Transferability Estimation (TE) method. We first analyze the drawbacks of using the existing TE algorithms for medical image segmentation and then design a source-free TE framework that considers both class consistency and feature variety for better estimation. Extensive experiments show that our method surpasses all current algorithms for transferability estimation in medical image segmentation. Code is available at https://github.com/EndoluminalSurgicalVision-IMR/CCFVComment: MICCAI2023(Early Accepted

Sequencing-enabled Hierarchical Cooperative CAV On-ramp Merging Control with Enhanced Stability and Feasibility

This paper develops a sequencing-enabled hierarchical connected automated vehicle (CAV) cooperative on-ramp merging control framework. The proposed framework consists of a two-layer design: the upper level control sequences the vehicles to harmonize the traffic density across mainline and on-ramp segments while enhancing lower-level control efficiency through a mixed-integer linear programming formulation. Subsequently, the lower-level control employs a longitudinal distributed model predictive control (MPC) supplemented by a virtual car-following (CF) concept to ensure asymptotic local stability, l_2 norm string stability, and safety. Proofs of asymptotic local stability and l_2 norm string stability are mathematically derived. Compared to other prevalent asymptotic local-stable MPC controllers, the proposed distributed MPC controller greatly expands the initial feasible set. Additionally, an auxiliary lateral control is developed to maintain lane-keeping and merging smoothness while accommodating ramp geometric curvature. To validate the proposed framework, multiple numerical experiments are conducted. Results indicate a notable outperformance of our upper-level controller against a distance-based sequencing method. Furthermore, the lower-level control effectively ensures smooth acceleration, safe merging with adequate spacing, adherence to proven longitudinal local and string stability, and rapid regulation of lateral deviations

Detaching and Boosting: Dual Engine for Scale-Invariant Self-Supervised Monocular Depth Estimation

Monocular depth estimation (MDE) in the self-supervised scenario has emerged as a promising method as it refrains from the requirement of ground truth depth. Despite continuous efforts, MDE is still sensitive to scale changes especially when all the training samples are from one single camera. Meanwhile, it deteriorates further since camera movement results in heavy coupling between the predicted depth and the scale change. In this paper, we present a scale-invariant approach for self-supervised MDE, in which scale-sensitive features (SSFs) are detached away while scale-invariant features (SIFs) are boosted further. To be specific, a simple but effective data augmentation by imitating the camera zooming process is proposed to detach SSFs, making the model robust to scale changes. Besides, a dynamic cross-attention module is designed to boost SIFs by fusing multi-scale cross-attention features adaptively. Extensive experiments on the KITTI dataset demonstrate that the detaching and boosting strategies are mutually complementary in MDE and our approach achieves new State-of-The-Art performance against existing works from 0.097 to 0.090 w.r.t absolute relative error. The code will be made public soon.Comment: Accepted by IEEE Robotics and Automation Letters (RAL

Neural Deformable Models for 3D Bi-Ventricular Heart Shape Reconstruction and Modeling from 2D Sparse Cardiac Magnetic Resonance Imaging

We propose a novel neural deformable model (NDM) targeting at the reconstruction and modeling of 3D bi-ventricular shape of the heart from 2D sparse cardiac magnetic resonance (CMR) imaging data. We model the bi-ventricular shape using blended deformable superquadrics, which are parameterized by a set of geometric parameter functions and are capable of deforming globally and locally. While global geometric parameter functions and deformations capture gross shape features from visual data, local deformations, parameterized as neural diffeomorphic point flows, can be learned to recover the detailed heart shape.Different from iterative optimization methods used in conventional deformable model formulations, NDMs can be trained to learn such geometric parameter functions, global and local deformations from a shape distribution manifold. Our NDM can learn to densify a sparse cardiac point cloud with arbitrary scales and generate high-quality triangular meshes automatically. It also enables the implicit learning of dense correspondences among different heart shape instances for accurate cardiac shape registration. Furthermore, the parameters of NDM are intuitive, and can be used by a physician without sophisticated post-processing. Experimental results on a large CMR dataset demonstrate the improved performance of NDM over conventional methods.Comment: Accepted by ICCV 202

Interfacial Properties of Monolayer and Bilayer MoS2 Contacts with Metals: Beyond the Energy Band Calculations

Although many prototype devices based on two-dimensional (2D) MoS2 have been fabricated and wafer scale growth of 2D MoS2 has been realized, the fundamental nature of 2D MoS2-metal contacts has not been well understood yet. We provide a comprehensive ab initio study of the interfacial properties of a series of monolayer (ML) and bilayer (BL) MoS2-metal contacts (metal = Sc, Ti, Ag, Pt, Ni, and Au). A comparison between the calculated and observed Schottky barrier heights (SBHs) suggests that many-electron effects are strongly suppressed in channel 2D MoS2 due to a charge transfer. The extensively adopted energy band calculation scheme fails to reproduce the observed SBHs in 2D MoS2-Sc interface. By contrast, an ab initio quantum transport device simulation better reproduces the observed SBH in the two types of contacts and highlights the importance of a higher level theoretical approach beyond the energy band calculation in the interface study. BL MoS2-metal contacts have a reduced SBH than ML MoS2-metal contacts due to the interlayer coupling and thus have a higher electron injection efficiency.Comment: 36 pages, 13 figures, 3 table

Does P-type Ohmic Contact Exist in WSe2-metal Interfaces?

Formation of low-resistance metal contacts is the biggest challenge that masks the intrinsic exceptional electronic properties of 2D WSe2 devices. We present the first comparative study of the interfacial properties between ML/BL WSe2 and Sc, Al, Ag, Au, Pd, and Pt contacts by using ab initio energy band calculations with inclusion of the spin-orbital coupling (SOC) effects and quantum transport simulations. The interlayer coupling tends to reduce both the electron and hole Schottky barrier heights (SBHs) and alters the polarity for WSe2-Au contact, while the SOC chiefly reduces the hole SBH. In the absence of the SOC, Pd contact has the smallest hole SBH with a value no less than 0.22 eV. Dramatically, Pt contact surpasses Pd contact and becomes p-type Ohmic or quasi-Ohmic contact with inclusion of the SOC. Our study provides a theoretical foundation for the selection of favorable metal electrodes in ML/BL WSe2 devices

Src-family protein tyrosine kinases: a promising target for treating chronic pain

Abstract Despite growing knowledge of the mechanisms of chronic pain, it remains a major challenge facing clinical practice. Src-family protein tyrosine kinases (SFKs), a group of non-receptor protein tyrosine kinases, have been implicated in neuronal development and synaptic plasticity. SFKs are critically central to various transmembrane receptors e.g. G-protein coupled receptor (GPCR), EphB receptor (EphBR), increased intracellular calcium, epidermal growth factor (EGF) and other growth factors that regulate the phosphorylation of N-methyl-D-aspartic acid receptor (NMDAR) 2B subunit, thus contributing to the development of chronic pain. SFKs have also been regarded as an important point of convergence of intracellular signaling components that regulate microglia functions and the immune response. Additionally, intrathecal administration of SFKs inhibitors significantly alleviates mechanical allodynia in different chronic pain models. Thus, here we reviewed the current evidence of the role of SFKs in the development of chronic pain caused by complete Freund's adjuvant (CFA) injection, peripheral nerve injury (PNI), streptozotocin (STZ) injection and bone metastasis. Moreover, the role of SFKs on the development of morphine tolerance has also been discussed. Management of SFKs therefore emerged as a potential therapeutic target for the treatment of chronic pain in terms of safety and efficacy. Key words Chronic pain; Src-family protein tyrosine kinases; N-methyl-D-aspartic acid receptor; Microglia