1 code implementation • 18 Mar 2024 • Juming Xiong, Ethan H. Nguyen, Yilin Liu, Ruining Deng, Regina N Tyree, Hernan Correa, Girish Hiremath, Yaohong Wang, Haichun Yang, Agnes B. Fogo, Yuankai Huo
Recently, circle representation has been introduced for medical imaging, designed specifically to enhance the detection of instance objects that are spherically shaped (e. g., cells, glomeruli, and nuclei).
no code implementations • 29 Feb 2024 • Ruining Deng, Quan Liu, Can Cui, Tianyuan Yao, Jialin Yue, Juming Xiong, Lining Yu, Yifei Wu, Mengmeng Yin, Yu Wang, Shilin Zhao, Yucheng Tang, Haichun Yang, Yuankai Huo
Understanding the anatomy of renal pathology is crucial for advancing disease diagnostics, treatment evaluation, and clinical research.
no code implementations • 11 Jan 2024 • Lucas W. Remedios, Shunxing Bao, Samuel W. Remedios, Ho Hin Lee, Leon Y. Cai, Thomas Li, Ruining Deng, Can Cui, Jia Li, Qi Liu, Ken S. Lau, Joseph T. Roland, Mary K. Washington, Lori A. Coburn, Keith T. Wilson, Yuankai Huo, Bennett A. Landman
In this paper, we propose to use inter-modality learning to label previously un-labelable cell types on virtual H&E.
no code implementations • 5 Sep 2023 • Muhao Liu, Chenyang Qi, Shunxing Bao, Quan Liu, Ruining Deng, Yu Wang, Shilin Zhao, Haichun Yang, Yuankai Huo
However, very few, if any, deep learning based approaches have been applied to kidney layer structure segmentation.
no code implementations • 20 Aug 2023 • Shunxing Bao, Sichen Zhu, Vasantha L Kolachala, Lucas W. Remedios, Yeonjoo Hwang, Yutong Sun, Ruining Deng, Can Cui, Yike Li, Jia Li, Joseph T. Roland, Qi Liu, Ken S. Lau, Subra Kugathasan, Peng Qiu, Keith T. Wilson, Lori A. Coburn, Bennett A. Landman, Yuankai Huo
This analysis is based on data collected at the two research institutes.
1 code implementation • 20 Aug 2023 • Xing Yao, Han Liu, Dewei Hu, Daiwei Lu, Ange Lou, Hao Li, Ruining Deng, Gabriel Arenas, Baris Oguz, Nadav Schwartz, Brett C Byram, Ipek Oguz
The method couples multi-box prompt augmentation and an aleatoric uncertainty-based false-negative (FN) and false-positive (FP) correction (FNPC) strategy.
1 code implementation • 18 Aug 2023 • Ruining Deng, Nazim Shaikh, Gareth Shannon, Yao Nie
Compared with single modality, which achieved c-index of 0. 5772 and 0. 5885 using solely tissue image data or RNA-seq data, respectively, the proposed fusion approach achieved c-index 0. 6587 in our experiment, showcasing the capability of assimilating modality-specific knowledge from varied modalities.
1 code implementation • 17 Aug 2023 • Yilin Liu, Ruining Deng, Juming Xiong, Regina N Tyree, Hernan Correa, Girish Hiremath, Yaohong Wang, Yuankai Huo
In this paper, we propose the multi-label CircleSnake model for instance segmentation on Eos.
1 code implementation • 11 Aug 2023 • Juming Xiong, Yilin Liu, Ruining Deng, Regina N Tyree, Hernan Correa, Girish Hiremath, Yaohong Wang, Yuankai Huo
Eosinophilic Esophagitis (EoE) is a chronic, immune/antigen-mediated esophageal disease, characterized by symptoms related to esophageal dysfunction and histological evidence of eosinophil-dominant inflammation.
no code implementations • 10 Aug 2023 • Xueyuan Li, Ruining Deng, Yucheng Tang, Shunxing Bao, Haichun Yang, Yuankai Huo
In this paper, we explore the potential of bypassing pixel-level delineation by employing the recent segment anything model (SAM) on weak box annotation in a zero-shot learning approach.
1 code implementation • 10 Aug 2023 • Haoju Leng, Ruining Deng, Shunxing Bao, Dazheng Fang, Bryan A. Millis, Yucheng Tang, Haichun Yang, Xiao Wang, Yifan Peng, Lipeng Wan, Yuankai Huo
The performance evaluation encompasses two key scenarios: (1) a pure CPU-based image analysis scenario ("CPU scenario"), and (2) a GPU-based deep learning framework scenario ("GPU scenario").
no code implementations • 10 Aug 2023 • Franklin Hu, Ruining Deng, Shunxing Bao, Haichun Yang, Yuankai Huo
While deep learning-based methods offer a solution for automatic segmentation, most suffer from a limitation: they are designed for and restricted to training on single-site, single-scale data.
1 code implementation • 10 Aug 2023 • Jiayuan Chen, Yu Wang, Ruining Deng, Quan Liu, Can Cui, Tianyuan Yao, Yilin Liu, Jianyong Zhong, Agnes B. Fogo, Haichun Yang, Shilin Zhao, Yuankai Huo
Podocytes, specialized epithelial cells that envelop the glomerular capillaries, play a pivotal role in maintaining renal health.
no code implementations • 3 Jul 2023 • Can Cui, Yaohong Wang, Shunxing Bao, Yucheng Tang, Ruining Deng, Lucas W. Remedios, Zuhayr Asad, Joseph T. Roland, Ken S. Lau, Qi Liu, Lori A. Coburn, Keith T. Wilson, Bennett A. Landman, Yuankai Huo
Many anomaly detection approaches, especially deep learning methods, have been recently developed to identify abnormal image morphology by only employing normal images during training.
no code implementations • 1 Jul 2023 • Can Cui, Ruining Deng, Quan Liu, Tianyuan Yao, Shunxing Bao, Lucas W. Remedios, Yucheng Tang, Yuankai Huo
The Segment Anything Model (SAM) is a recently proposed prompt-based segmentation model in a generic zero-shot segmentation approach.
no code implementations • 5 Jun 2023 • Tianyuan Yao, Francois Rheault, Leon Y Cai, Vishwesh Nath, Zuhayr Asad, Nancy Newlin, Can Cui, Ruining Deng, Karthik Ramadass, Andrea Shafer, Susan Resnick, Kurt Schilling, Bennett A. Landman, Yuankai Huo
From the experimental results, the proposed data-driven framework outperforms the existing benchmarks in repeated fODF estimation.
1 code implementation • 31 May 2023 • Ruining Deng, Yanwei Li, Peize Li, Jiacheng Wang, Lucas W. Remedios, Saydolimkhon Agzamkhodjaev, Zuhayr Asad, Quan Liu, Can Cui, Yaohong Wang, Yihan Wang, Yucheng Tang, Haichun Yang, Yuankai Huo
The contribution of this paper is threefold: (1) We proposed a molecular-empowered learning scheme for multi-class cell segmentation using partial labels from lay annotators; (2) The proposed method integrated Giga-pixel level molecular-morphology cross-modality registration, molecular-informed annotation, and molecular-oriented segmentation model, so as to achieve significantly superior performance via 3 lay annotators as compared with 2 experienced pathologists; (3) A deep corrective learning (learning with imperfect label) method is proposed to further improve the segmentation performance using partially annotated noisy data.
1 code implementation • 23 May 2023 • Haoju Leng, Ruining Deng, Zuhayr Asad, R. Michael Womick, Haichun Yang, Lipeng Wan, Yuankai Huo
Our proposed method's innovative contribution is two-fold: (1) a Docker is released for an end-to-end slide-wise multi-tissue segmentation for WSIs; and (2) the pipeline is deployed on a GPU to accelerate the prediction, achieving better segmentation quality in less time.
1 code implementation • 19 May 2023 • Peize Li, Ruining Deng, Yuankai Huo
In this paper, we provide a Docker for an end-to-end 3D slide-wise registration pipeline on needle biopsy serial sections in a multi-stain paradigm.
no code implementations • 24 Apr 2023 • Lucas W. Remedios, Leon Y. Cai, Samuel W. Remedios, Karthik Ramadass, Aravind Krishnan, Ruining Deng, Can Cui, Shunxing Bao, Lori A. Coburn, Yuankai Huo, Bennett A. Landman
The M1 Ultra SoC was able to train the model directly on gigapixel images (16000$\times$64000 pixels, 1. 024 billion pixels) with a batch size of 1 using over 100 GB of unified memory for the process at an average speed of 1 minute and 21 seconds per batch with Tensorflow 2/Keras.
no code implementations • 9 Apr 2023 • Ruining Deng, Can Cui, Quan Liu, Tianyuan Yao, Lucas W. Remedios, Shunxing Bao, Bennett A. Landman, Lee E. Wheless, Lori A. Coburn, Keith T. Wilson, Yaohong Wang, Shilin Zhao, Agnes B. Fogo, Haichun Yang, Yucheng Tang, Yuankai Huo
However, it does not consistently achieve satisfying performance for dense instance object segmentation, even with 20 prompts (clicks/boxes) on each image.
1 code implementation • 1 Apr 2023 • Ruining Deng, Can Cui, Lucas W. Remedios, Shunxing Bao, R. Michael Womick, Sophie Chiron, Jia Li, Joseph T. Roland, Ken S. Lau, Qi Liu, Keith T. Wilson, Yaohong Wang, Lori A. Coburn, Bennett A. Landman, Yuankai Huo
Analyzing high resolution whole slide images (WSIs) with regard to information across multiple scales poses a significant challenge in digital pathology.
1 code implementation • 2 Nov 2022 • Ethan H. Nguyen, Haichun Yang, Zuhayr Asad, Ruining Deng, Agnes B. Fogo, Yuankai Huo
Circle representation has recently been introduced as a medical imaging optimized representation for more effective instance object detection on ball-shaped medical objects.
1 code implementation • 30 Aug 2022 • Tianyuan Yao, Chang Qu, Jun Long, Quan Liu, Ruining Deng, Yuanhan Tian, Jiachen Xu, Aadarsh Jha, Zuhayr Asad, Shunxing Bao, Mengyang Zhao, Agnes B. Fogo, Bennett A. Landman, Haichun Yang, Catie Chang, Yuankai Huo
In order to extract and separate compound figures into usable individual images for downstream learning, we propose a simple compound figure separation (SimCFS) framework without using the traditionally required detection bounding box annotations, with a new loss function and a hard case simulation.
1 code implementation • 15 Aug 2022 • Ruining Deng, Can Cui, Lucas W. Remedios, Shunxing Bao, R. Michael Womick, Sophie Chiron, Jia Li, Joseph T. Roland, Ken S. Lau, Qi Liu, Keith T. Wilson, Yaohong Wang, Lori A. Coburn, Bennett A. Landman, Yuankai Huo
Multi-instance learning (MIL) is widely used in the computer-aided interpretation of pathological Whole Slide Images (WSIs) to solve the lack of pixel-wise or patch-wise annotations.
1 code implementation • 27 Jun 2022 • Ruining Deng, Quan Liu, Can Cui, Tianyuan Yao, Jun Long, Zuhayr Asad, R. Michael Womick, Zheyu Zhu, Agnes B. Fogo, Shilin Zhao, Haichun Yang, Yuankai Huo
The contribution of this paper is three-fold: (1) a novel scale-aware controller is proposed to generalize the dynamic neural network from single-scale to multi-scale; (2) semi-supervised consistency regularization of pseudo-labels is introduced to model the inter-scale correlation of unannotated tissue types into a single end-to-end learning paradigm; and (3) superior scale-aware generalization is evidenced by directly applying a model trained on human kidney images to mouse kidney images, without retraining.
no code implementations • 8 Mar 2022 • Can Cui, Han Liu, Quan Liu, Ruining Deng, Zuhayr Asad, Yaohong WangShilin Zhao, Haichun Yang, Bennett A. Landman, Yuankai Huo
Thus, there are still open questions on how to effectively predict brain cancer survival from the incomplete radiological, pathological, genomic, and demographic data (e. g., one or more modalities might not be collected for a patient).
1 code implementation • 23 Dec 2021 • Ruining Deng, Quan Liu, Can Cui, Zuhayr Asad, Haichun Yang, Yuankai Huo
Computer-assisted quantitative analysis on Giga-pixel pathology images has provided a new avenue in histology examination.
1 code implementation • 22 Oct 2021 • Ethan H. Nguyen, Haichun Yang, Ruining Deng, Yuzhe Lu, Zheyu Zhu, Joseph T. Roland, Le Lu, Bennett A. Landman, Agnes B. Fogo, Yuankai Huo
Compared with the conventional bounding box representation, the proposed bounding circle representation innovates in three-fold: (1) it is optimized for ball-shaped biomedical objects; (2) The circle representation reduced the degree of freedom compared with box representation; (3) It is naturally more rotation invariant.
Ranked #1 on Medical Object Detection on MoNuSeg 2018
1 code implementation • 19 Jul 2021 • Tianyuan Yao, Chang Qu, Quan Liu, Ruining Deng, Yuanhan Tian, Jiachen Xu, Aadarsh Jha, Shunxing Bao, Mengyang Zhao, Agnes B. Fogo, Bennett A. Landman, Catie Chang, Haichun Yang, Yuankai Huo
Our technical contribution is three-fold: (1) we introduce a new side loss that is designed for compound figure separation; (2) we introduce an intra-class image augmentation method to simulate hard cases; (3) the proposed framework enables an efficient deployment to new classes of images, without requiring resource extensive bounding box annotations.
no code implementations • 22 Jun 2021 • Mengyang Zhao, Quan Liu, Aadarsh Jha, Ruining Deng, Tianyuan Yao, Anita Mahadevan-Jansen, Matthew J. Tyska, Bryan A. Millis, Yuankai Huo
Recently, pixel embedding-based cell instance segmentation and tracking provided a neat and generalizable computing paradigm for understanding cellular dynamics.
1 code implementation • 9 Mar 2021 • Quan Liu, Peter C. Louis, Yuzhe Lu, Aadarsh Jha, Mengyang Zhao, Ruining Deng, Tianyuan Yao, Joseph T. Roland, Haichun Yang, Shilin Zhao, Lee E. Wheless, Yuankai Huo
The contribution of the paper is three-fold: (1) The proposed SimTriplet method takes advantage of the multi-view nature of medical images beyond self-augmentation; (2) The method maximizes both intra-sample and inter-sample similarities via triplets from positive pairs, without using negative samples; and (3) The recent mix precision training is employed to advance the training by only using a single GPU with 16GB memory.
1 code implementation • 16 Jan 2021 • Yuzhe Lu, Haichun Yang, Zheyu Zhu, Ruining Deng, Agnes B. Fogo, Yuankai Huo
Different from the recently proposed CutMix method, the CircleMix augmentation is optimized for the ball-shaped biomedical objects, such as glomeruli.
no code implementations • 3 Jan 2021 • Quan Liu, Isabella M. Gaeta, Mengyang Zhao, Ruining Deng, Aadarsh Jha, Bryan A. Millis, Anita Mahadevan-Jansen, Matthew J. Tyska, Yuankai Huo
Contribution: The contribution of this paper is three-fold: (1) the proposed method aggregates adversarial simulations and single-stage pixel-embedding based deep learning; (2) the method is assessed with both the cellular (i. e., HeLa cells) and subcellular (i. e., microvilli) objects; and (3) to the best of our knowledge, this is the first study to explore annotation-free instance segmentation and tracking study for microscope videos.
1 code implementation • 2 Nov 2020 • Ruining Deng, Quan Liu, Shunxing Bao, Aadarsh Jha, Catie Chang, Bryan A. Millis, Matthew J. Tyska, Yuankai Huo
Our contribution is three-fold: (1) we approach the weakly supervised segmentation from a novel codebook learning perspective; (2) the CaCL algorithm segments diffuse image patterns rather than focal objects; and (3) the proposed algorithm is implemented in a multi-task framework based on Vector Quantised-Variational AutoEncoder (VQ-VAE) via joint image reconstruction, classification, feature embedding, and segmentation.
no code implementations • 2 Nov 2020 • Quan Liu, Isabella M. Gaeta, Mengyang Zhao, Ruining Deng, Aadarsh Jha, Bryan A. Millis, Anita Mahadevan-Jansen, Matthew J. Tyska, Yuankai Huo
Instance object segmentation and tracking provide comprehensive quantification of objects across microscope videos.
1 code implementation • 28 Jul 2020 • Zheyu Zhu, Yuzhe Lu, Ruining Deng, Haichun Yang, Agnes B. Fogo, Yuankai Huo
Inspired by the recent "human-in-the-loop" strategy, we developed EasierPath, an open-source tool to integrate human physicians and deep learning algorithms for efficient large-scale pathological image quantification as a loop.
2 code implementations • 7 Jul 2020 • Aadarsh Jha, Haichun Yang, Ruining Deng, Meghan E. Kapp, Agnes B. Fogo, Yuankai Huo
In this paper, we assess if the end-to-end instance segmentation framework is optimal for high-resolution WSI objects by comparing Mask-RCNN with our proposed detect-then-segment framework.
1 code implementation • 10 Jun 2020 • Ruining Deng, Haichun Yang, Aadarsh Jha, Yuzhe Lu, Peng Chu, Agnes B. Fogo, Yuankai Huo
However, the 3D identification and association of large-scale glomeruli on renal pathology is challenging due to large tissue deformation, missing tissues, and artifacts from WSI.
1 code implementation • 3 Jun 2020 • Haichun Yang, Ruining Deng, Yuzhe Lu, Zheyu Zhu, Ye Chen, Joseph T. Roland, Le Lu, Bennett A. Landman, Agnes B. Fogo, Yuankai Huo
In this work, we propose CircleNet, a simple anchor-free detection method with circle representation for detection of the ball-shaped glomerulus.