Data-Centric Diet: Effective Multi-center Dataset Pruning for Medical Image Segmentation

This paper seeks to address the dense labeling problems where a significant fraction of the dataset can be pruned without sacrificing much accuracy. We observe that, on standard medical image segmentation benchmarks, the loss gradient norm-based metrics of individual training examples applied in image classification fail to identify the important samples. To address this issue, we propose a data pruning method by taking into consideration the training dynamics on target regions using Dynamic Average Dice (DAD) score. To the best of our knowledge, we are among the first to address the data importance in dense labeling tasks in the field of medical image analysis, making the following contributions: (1) investigating the underlying causes with rigorous empirical analysis, and (2) determining effective data pruning approach in dense labeling problems. Our solution can be used as a strong yet simple baseline to select important examples for medical image segmentation with combined data sources.Comment: Accepted by ICML workshops 202

Learning to In-paint: Domain Adaptive Shape Completion for 3D Organ Segmentation

We aim at incorporating explicit shape information into current 3D organ segmentation models. Different from previous works, we formulate shape learning as an in-painting task, which is named Masked Label Mask Modeling (MLM). Through MLM, learnable mask tokens are fed into transformer blocks to complete the label mask of organ. To transfer MLM shape knowledge to target, we further propose a novel shape-aware self-distillation with both in-painting reconstruction loss and pseudo loss. Extensive experiments on five public organ segmentation datasets show consistent improvements over prior arts with at least 1.2 points gain in the Dice score, demonstrating the effectiveness of our method in challenging unsupervised domain adaptation scenarios including: (1) In-domain organ segmentation; (2) Unseen domain segmentation and (3) Unseen organ segmentation. We hope this work will advance shape analysis and geometric learning in medical imaging

Complex semiclassical theory for non-Hermitian quantum systems

Non-Hermitian quantum systems exhibit fascinating characteristics such as non-Hermitian topological phenomena and skin effect, yet their studies are limited by the intrinsic difficulties associated with their eigenvalue problems, especially in larger systems and higher dimensions. In Hermitian systems, the semiclassical theory has played an active role in analyzing spectrum, eigenstate, phase, transport properties, etc. Here, we establish a complex semiclassical theory applicable to non-Hermitian quantum systems by an analytical continuation of the physical variables such as momentum, position, time, and energy in the equations of motion and quantization condition to the complex domain. Further, we propose a closed-orbit scheme and physical meaning under such complex variables. We demonstrate that such a framework straightforwardly yields complex energy spectra and quantum states, topological phases and transitions, and even the skin effect in non-Hermitian quantum systems, presenting an unprecedented perspective toward nontrivial non-Hermitian physics, even with larger systems and higher dimensions.Comment: 16 pages, 10 figure

Optical sensors using chaotic correlation fiber loop ring down

We have proposed a novel optical sensor scheme based on chaotic correlation fiber loop ring down (CCFLRD). In contrast to the well-known FLRD spectroscopy, where pulsed laser is injected to fiber loop and ring down time is measured, the proposed CCFLRD uses a chaotic laser to drive a fiber loop and measures autocorrelation coefficient ring down time of chaotic laser. The fundamental difference enables us to avoid using long fiber loop as required in pulsed FLRD, and thus generates higher sensitivity. A strain sensor has been developed to validate the CCFLRD concept. Theoretical and experiment results demonstrate that the proposed method is able to enhance sensitivity by more than two orders of magnitude comparing to the existing FLRD method. We believe the proposed method could find great potential applications for chemical, medical, and physical sensing