The paper presents Libo Zhang’s algorithmic solution for the autoPETV Grand Challenge, focusing on interactive lesion segmentation in whole-body PET/CT scans. The method encodes user scribbles as two additional input channels and trains a large residual‑encoder U‑Net (≈140 M parameters) through a three‑phase curriculum over 4000 epochs, progressively moving from fully automatic segmentation to handling user‑provided scribbles and finally correcting its own mistakes via simulated error‑driven steps. Using 1811 studies for training and an ensemble of five‑fold checkpoints, the approach achieves a mean AUC‑Dice of 3.836 and a mean AUC‑DMM of 3.869 in interactive five‑fold cross‑validation, with significant gains from the first corrective scribble.
By Libo Zhang
The article presents a method that uses persistent homology and a support vector machine to automatically classify teeth and diagnose pathologies in CBCT scans. It reports high accuracy, achieving 97.67% for tooth labeling and 96.77% for diagnostics, surpassing a CNN baseline. The approach aims to reduce the labor-intensive analysis of detailed 3‑D dental images.
By Leon Dahlmeier, Sara Kali\v{s}nik, Albert Mehl, Bastian Rieck
arXiv:2602.07008v5 Announce Type: replace
Abstract: Reliable models should not only predict correctly, but also base their decisions on acceptable evidence. However, conventional supervised learning...
By Ruoyu Chen, Shangquan Sun, Xiaoqing Guo, Kangwei Liu, Sanyi Zhang, Zhangcheng Wang, Shiming Liu, Qunli Zhang, Wei Wang, Hua Zhang, Xiaochun Cao
The paper introduces ClusterMixer, a token mixer based on hierarchical clustering in hyperbolic space, designed to be transparent and interpretable. It forms the core of a new vision backbone called HCFormer, which incorporates multiple clustering strategies to maintain strong performance. Experiments show HCFormer surpasses existing backbones on tasks such as image classification, object detection, instance segmentation, and semantic segmentation.
By Jianan Wei, Guikun Chen, Zhiyuan Weng, Chunchao Guo, Yujia Wang, Wenguan Wang
The paper introduces Segment Anything Small (SAS), a data‑augmentation method that improves deep‑learning segmentation of small anatomical structures in ultrasound images. SAS uses two transformations: resizing and embedding organ thumbnails into a black background to vary organ scale, and adding noise to regions of interest to mimic tissue texture variability. Experiments on one internal and five external datasets show Dice score gains up to 0.35, with an average improvement of 0.16, and demonstrate that SAS enhances model robustness and generalizability without adding hallucinations or artifacts.
By Danielle L. Ferreira, Ahana Gangopadhyay, Hsi-Ming Chang, Ravi Soni, Gopal Avinash
arXiv:2608.21869v1 Announce Type: cross
Abstract: Text-to-image (T2I) diffusion models offer powerful visual generation, but their controllability creates a critical safety challenge: adversarial pro...
By Shreyash Dhoot, Paras Dhiman, Arsh Abbas Naqvi, Aranbi Dutta, Aman Chadha, Vinija Jain, Amitava Das
arXiv:2608.23234v1 Announce Type: new
Abstract: In this technical report, we present a training-free framework for audio-guided video object segmentation, which integrates Multimodal Large Language M...
By Liangtao Shi, Jinxia Xie, Xiantao Hu, Ting Liu
arXiv:2608.23011v1 Announce Type: cross
Abstract: Graph-based retrieval-augmented generation (RAG) provides a scalable paradigm for long-video understanding, but existing systems typically inherit a...
By Zhe Jin, Zhimin Lin, Bin Zheng, Junhua Fang, Huihua Yang
arXiv:2510.14383v4 Announce Type: replace
Abstract: Accurate brain tumor segmentation is significant for clinical diagnosis and treatment but remains challenging due to tumor heterogeneity. Mamba-bas...
By Danish Ali, Ajmal Mian, Naveed Akhtar, Ghulam Mubashar Hassan
arXiv:2608.22398v1 Announce Type: cross
Abstract: Reliably tracking moving deformable linear objects (DLOs) while simultaneously ensuring robustness, accuracy, and temporally consistent state estimat...
By Annalena Hartmann, Priyamvada Ajithkumar, Patrick Br\"undl, J\"org Franke
arXiv:2608.22337v1 Announce Type: cross
Abstract: Speech-guided referring video object segmentation aims to recover the mask tracks of objects specified by a spoken motion description. Here, speech c...
By Jinxing Zhou, Suiyi Zhao, Yanghao Zhou, Ruohao Guo
arXiv:2608.22281v1 Announce Type: cross
Abstract: Medical image segmentation requires high accuracy and robustness, yet practical commercial deployment also demands privacy preservation and computati...
By Bin Dong, Jinghong Chen
The study presents a vision‑language model pipeline that estimates dynamic, triaxial, bilateral external hand forces during manual material handling tasks using only RGB video and known box mass. By combining text‑guided ROI localization, pretrained vision‑transformer features, and transformer‑based temporal regression, the model achieved root mean square errors of about 4.7–5.6 N for horizontal and mediolateral forces and 10.6–11.0 N for vertical forces across various camera setups. The approach demonstrated that including the handled object as a second ROI and using multi‑camera capture improved peak‑force estimation, showing the feasibility of sensor‑free force estimation for occupational exposure assessment.
By Mohammad Sadra Rajabi, Aanuoluwapo Ojelade, Sunwook Kim, Maury A. Nussbaum
arXiv:2607.14935v2 Announce Type: replace
Abstract: Recent advances in video understanding have spanned motion, long video, and streaming interaction, driving this field toward real-world application...
By Xinhao Li, Yuhan Zhu, Xiangyu Zeng, Yuhao Dong, Haoning Wu, Zhiqiu Zhang, Yuandong Yang, Changlian Ma, Qingyu Zhang, Yansong Shi, Xinyu Chen, Haoran Chen, Zizheng Huang, Jun Zhang, Kun Ouyang, Lin Sui, Ziang Yan, Yicheng Xu, Chenting Wang, Yinan He, Hongjie Zhang, Yi Wang, Yu Qiao, Yali Wang, Ziwei Liu, Kai Chen, Limin Wang
arXiv:2607.05176v3 Announce Type: replace
Abstract: Small object detection (SOD) remains a challenging task in real-world applications. Despite recent advances, existing detectors remain limited by r...
By Aiwen Liu, Chengguang Zhu, Gang Wang, Dandan Zhu, Haodong Lin, Yan Wang, Huiyu Zhou, Zhengyi Pan
arXiv:2608.23432v1 Announce Type: new
Abstract: Accurate organ-at-risk segmentation is essential for radiotherapy planning, but reviewing segmentations is time-consuming and subjective. We investigat...
By Clea Dronne, Catharine H Clark, Xavier Loizeau, Elizabeth Miles, Peter Hoskin, Jamie R McClelland
arXiv:2608.22465v1 Announce Type: new
Abstract: High-fidelity free-viewpoint video (FVV) and interactive rendering increasingly rely on explicit Gaussian representations, yet practical deployment rem...
By Xinhui Liu, Lei Liu, Zhenghao Chen, Lebin Zhou, Wei Wang, Wei Jiang
arXiv:2608.22314v1 Announce Type: new
Abstract: Many image processing methods such as corner detection, optical flow and iterative enhancement make use of image tensors. Generally, these tensors are...
By Freddie {\AA}str\"om, Michael Felsberg
arXiv:2608.21970v1 Announce Type: new
Abstract: Two-photon calcium imaging presents a challenging setting for foundation models: image appearance varies substantially across recordings and experiment...
By Salvatore Calcagno, Marco Finocchiaro, Giovanni Bellitto, Daniela Giordano, Concetto Spampinato, Federica Proietto Salanitri
arXiv:2608.23014v1 Announce Type: new
Abstract: 3D brain MRI generation has made significant advances in medical imaging, simulation, and controllable anatomical analysis. However, existing generativ...
By Huiwen Han, Lulin Liu, Bangya Liu, Yuanhao Cai, Nuo Chen, Xiaoqing Wang, Ziqian Xie, Chenyu You, Shuiwang Ji, Degui Zhi, Zhiwen Fan