no code implementations • 19 Jan 2024 • Mingjian Li, Mingyuan Meng, Michael Fulham, David Dagan Feng, Lei Bi, Jinman Kim
These learned image representations can be transferred to and benefit various downstream medical vision tasks such as disease classification and segmentation.
1 code implementation • 28 Nov 2023 • Mingyuan Meng, Yuxin Xue, Dagan Feng, Lei Bi, Jinman Kim
This textural information is crucial for medical dense prediction as it can differentiate the subtle human anatomy in medical images.
1 code implementation • 28 Oct 2023 • Hao Wang, Euijoon Ahn, Lei Bi, Jinman Kim
The clinical diagnosis of skin lesion involves the analysis of dermoscopic and clinical modalities.
no code implementations • 24 Oct 2023 • Yuxin Xue, Lei Bi, Yige Peng, Michael Fulham, David Dagan Feng, Jinman Kim
We introduce (1) An adaptive residual estimation mapping mechanism, AE-Net, designed to dynamically rectify the preliminary synthesized PET images by taking the residual map between the low-dose PET and synthesized output as the input, and (2) A self-supervised pre-training strategy to enhance the feature representation of the coarse generator.
1 code implementation • 11 Sep 2023 • Mingyuan Meng, Michael Fulham, Dagan Feng, Lei Bi, Jinman Kim
However, DNN-based registration needs to explore the spatial information of each image and fuse this information to characterize spatial correspondence.
1 code implementation • 7 Jul 2023 • Mingyuan Meng, Lei Bi, Michael Fulham, Dagan Feng, Jinman Kim
In view of this, we propose a merging-diverging learning framework for survival prediction from multi-modality images.
1 code implementation • 7 Jul 2023 • Mingyuan Meng, Lei Bi, Michael Fulham, Dagan Feng, Jinman Kim
Recently, Non-Iterative Coarse-to-finE (NICE) registration methods have been proposed to perform coarse-to-fine registration in a single network and showed advantages in both registration accuracy and runtime.
2 code implementations • 17 May 2023 • Mingyuan Meng, Bingxin Gu, Michael Fulham, Shaoli Song, Dagan Feng, Lei Bi, Jinman Kim
Instead of adopting MTL, we propose a novel Segmentation-to-Survival Learning (SSL) strategy, where our AdaMSS is trained for tumor segmentation and survival prediction sequentially in two stages.
no code implementations • 3 Apr 2023 • Yuxin Xue, Yige Peng, Lei Bi, Dagan Feng, Jinman Kim
We compared our method to the state-of-the-art methods on whole-body PET with different dose reduction factors (DRFs).
1 code implementation • 15 Nov 2022 • Mingyuan Meng, Lei Bi, Dagan Feng, Jinman Kim
In this study, we focus on brain tumor sequence registration between pre-operative and follow-up Magnetic Resonance Imaging (MRI) scans of brain glioma patients, in the context of Brain Tumor Sequence Registration challenge (BraTS-Reg 2022).
2 code implementations • 10 Nov 2022 • Mingyuan Meng, Lei Bi, Dagan Feng, Jinman Kim
Recently, deep learning methods have been proposed to perform end-to-end outcome prediction so as to remove the reliance on manual segmentation.
no code implementations • 28 Oct 2022 • Lei Bi, Michael Fulham, Shaoli Song, David Dagan Feng, Jinman Kim
We also introduced a hyper connected fusion to fuse the contextual and complementary image features across multiple transformers in an iterative manner.
1 code implementation • 16 Sep 2022 • Yige Peng, Jinman Kim, Dagan Feng, Lei Bi
In this study, we introduce a false positive reduction network to overcome this limitation.
1 code implementation • 25 Jun 2022 • Mingyuan Meng, Lei Bi, Dagan Feng, Jinman Kim
Recently, iterative deep registration methods have been used to alleviate this limitation, where the transformations are iteratively learned in a coarse-to-fine manner.
no code implementations • 13 Dec 2021 • Bhakti Baheti, Satrajit Chakrabarty, Hamed Akbari, Michel Bilello, Benedikt Wiestler, Julian Schwarting, Evan Calabrese, Jeffrey Rudie, Syed Abidi, Mina Mousa, Javier Villanueva-Meyer, Brandon K. K. Fields, Florian Kofler, Russell Takeshi Shinohara, Juan Eugenio Iglesias, Tony C. W. Mok, Albert C. S. Chung, Marek Wodzinski, Artur Jurgas, Niccolo Marini, Manfredo Atzori, Henning Muller, Christoph Grobroehmer, Hanna Siebert, Lasse Hansen, Mattias P. Heinrich, Luca Canalini, Jan Klein, Annika Gerken, Stefan Heldmann, Alessa Hering, Horst K. Hahn, Mingyuan Meng, Lei Bi, Dagan Feng, Jinman Kim, Ramy A. Zeineldin, Mohamed E. Karar, Franziska Mathis-Ullrich, Oliver Burgert, Javid Abderezaei, Aymeric Pionteck, Agamdeep Chopra, Mehmet Kurt, Kewei Yan, Yonghong Yan, Zhe Tang, Jianqiang Ma, Sahar Almahfouz Nasser, Nikhil Cherian Kurian, Mohit Meena, Saqib Shamsi, Amit Sethi, Nicholas J. Tustison, Brian B. Avants, Philip Cook, James C. Gee, Lin Tian, Hastings Greer, Marc Niethammer, Andrew Hoopes, Malte Hoffmann, Adrian V. Dalca, Stergios Christodoulidis, Theo Estiene, Maria Vakalopoulou, Nikos Paragios, Daniel S. Marcus, Christos Davatzikos, Aristeidis Sotiras, Bjoern Menze, Spyridon Bakas, Diana Waldmannstetter
Registration of longitudinal brain MRI scans containing pathologies is challenging due to dramatic changes in tissue appearance.
2 code implementations • 30 Sep 2021 • Yuyu Guo, Lei Bi, Dongming Wei, Liyun Chen, Zhengbin Zhu, Dagan Feng, Ruiyan Zhang, Qian Wang, Jinman Kim
In the first stage, we process the raw dense image to extract sparse landmarks to represent the target organ anatomical topology and discard the redundant information that is unnecessary for motion estimation.
2 code implementations • 16 Sep 2021 • Mingyuan Meng, Bingxin Gu, Lei Bi, Shaoli Song, David Dagan Feng, Jinman Kim
However, the models using the whole target regions will also include non-relevant background information, while the models using segmented tumor regions will disregard potentially prognostic information existing out of primary tumors (e. g., local lymph node metastasis and adjacent tissue invasion).
no code implementations • 9 Jun 2021 • Kai-Chieh Liang, Lei Bi, Ashnil Kumar, Michael Fulham, Jinman Kim
Our ST-DSNN learns and accumulates image features from the PET images done over time.
no code implementations • 23 Apr 2021 • Yige Peng, Lei Bi, Ashnil Kumar, Michael Fulham, Dagan Feng, Jinman Kim
Most CNNs are designed for single-modality imaging data (CT or PET alone) and do not exploit the information embedded in PET-CT where there is a combination of an anatomical and functional imaging modality.
no code implementations • 1 Apr 2021 • Xiaohang Fu, Lei Bi, Ashnil Kumar, Michael Fulham, Jinman Kim
Further, there is not a method to exploit the intercategory relationships in the 7PC.
2 code implementations • 9 Mar 2021 • Mingyuan Meng, Lei Bi, Michael Fulham, David Dagan Feng, Jinman Kim
In this study, we propose an Appearance Adjustment Network (AAN) to enhance the adaptability of DLRs to appearance variations.
no code implementations • 9 Mar 2021 • Bingxin Gu, Mingyuan Meng, Lei Bi, Jinman Kim, David Dagan Feng, Shaoli Song
Methods: A total of 257 patients (170/87 in internal/external cohorts) with advanced NPC (TNM stage III or IVa) were enrolled.
no code implementations • 5 Mar 2021 • Xiaohang Fu, Lei Bi, Ashnil Kumar, Michael Fulham, Jinman Kim
Furthermore, lung nodules are often heterogeneous in the cross-sectional image slices of a 3D volume.
no code implementations • 29 Jul 2020 • Xiaohang Fu, Lei Bi, Ashnil Kumar, Michael Fulham, Jinman Kim
Our MSAM can be applied to common backbone architectures and trained end-to-end.
no code implementations • 29 Jul 2020 • Lei Bi, Jinman Kim, Tingwei Su, Michael Fulham, David Dagan Feng, Guang Ning
The application of CNNs, to adrenal masses is challenging due to large intra-class variations, large inter-class similarities and imbalanced training data due to the size of the mass lesions.
no code implementations • 12 Jul 2020 • Yige Peng, Lei Bi, Michael Fulham, Dagan Feng, Jinman Kim
'Radiomics' is a method that extracts mineable quantitative features from radiographic images.
1 code implementation • CVPR 2020 • Yuyu Guo, Lei Bi, Euijoon Ahn, Dagan Feng, Qian Wang, Jinman Kim
SVIN introduces dual networks: first is the spatiotemporal motion network that leverages the 3D convolutional neural network (CNN) for unsupervised parametric volumetric registration to derive spatiotemporal motion field from two-image volumes; the second is the sequential volumetric interpolation network, which uses the derived motion field to interpolate image volumes, together with a new regression-based module to characterize the periodic motion cycles in functional organ structures.
1 code implementation • 2 Dec 2019 • Mingyuan Meng, Xingyu Yang, Lei Bi, Jinman Kim, Shanlin Xiao, Zhiyi Yu
Most STDP-based SNNs adopted a slow-learning Fully-Connected (FC) architecture and used a sub-optimal vote-based scheme for spike decoding.
no code implementations • 13 Feb 2019 • Lei Bi, Yuyu Guo, Qian Wang, Dagan Feng, Michael Fulham, Jinman Kim
Our approach leverages deep residual architectures and FCNs and learns and infers the location of the optic cup and disk in a step-wise manner with fine-grained details.
6 code implementations • 13 Jan 2019 • Patrick Bilic, Patrick Christ, Hongwei Bran Li, Eugene Vorontsov, Avi Ben-Cohen, Georgios Kaissis, Adi Szeskin, Colin Jacobs, Gabriel Efrain Humpire Mamani, Gabriel Chartrand, Fabian Lohöfer, Julian Walter Holch, Wieland Sommer, Felix Hofmann, Alexandre Hostettler, Naama Lev-Cohain, Michal Drozdzal, Michal Marianne Amitai, Refael Vivantik, Jacob Sosna, Ivan Ezhov, Anjany Sekuboyina, Fernando Navarro, Florian Kofler, Johannes C. Paetzold, Suprosanna Shit, Xiaobin Hu, Jana Lipková, Markus Rempfler, Marie Piraud, Jan Kirschke, Benedikt Wiestler, Zhiheng Zhang, Christian Hülsemeyer, Marcel Beetz, Florian Ettlinger, Michela Antonelli, Woong Bae, Míriam Bellver, Lei Bi, Hao Chen, Grzegorz Chlebus, Erik B. Dam, Qi Dou, Chi-Wing Fu, Bogdan Georgescu, Xavier Giró-i-Nieto, Felix Gruen, Xu Han, Pheng-Ann Heng, Jürgen Hesser, Jan Hendrik Moltz, Christian Igel, Fabian Isensee, Paul Jäger, Fucang Jia, Krishna Chaitanya Kaluva, Mahendra Khened, Ildoo Kim, Jae-Hun Kim, Sungwoong Kim, Simon Kohl, Tomasz Konopczynski, Avinash Kori, Ganapathy Krishnamurthi, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Li, John Lowengrub, Jun Ma, Klaus Maier-Hein, Kevis-Kokitsi Maninis, Hans Meine, Dorit Merhof, Akshay Pai, Mathias Perslev, Jens Petersen, Jordi Pont-Tuset, Jin Qi, Xiaojuan Qi, Oliver Rippel, Karsten Roth, Ignacio Sarasua, Andrea Schenk, Zengming Shen, Jordi Torres, Christian Wachinger, Chunliang Wang, Leon Weninger, Jianrong Wu, Daguang Xu, Xiaoping Yang, Simon Chun-Ho Yu, Yading Yuan, Miao Yu, Liping Zhang, Jorge Cardoso, Spyridon Bakas, Rickmer Braren, Volker Heinemann, Christopher Pal, An Tang, Samuel Kadoury, Luc Soler, Bram van Ginneken, Hayit Greenspan, Leo Joskowicz, Bjoern Menze
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.
no code implementations • 23 Jul 2018 • Lei Bi, Dagan Feng, Jinman Kim
Segmentation of skin lesions is considered as an important step in computer aided diagnosis (CAD) for automated melanoma diagnosis.
2 code implementations • 31 Jul 2017 • Lei Bi, Jinman Kim, Ashnil Kumar, Dagan Feng, Michael Fulham
Positron emission tomography (PET) image synthesis plays an important role, which can be used to boost the training data for computer aided diagnosis systems.
no code implementations • 10 Apr 2017 • Lei Bi, Jinman Kim, Ashnil Kumar, Dagan Feng
Recently, deep learning methods based on fully convolutional networks (FCNs) have been successful in many segmentation problems primarily because they leverage a large labelled dataset to hierarchically learn the features that best correspond to the shallow visual appearance as well as the deep semantics of the areas to be segmented.
no code implementations • 12 Mar 2017 • Lei Bi, Jinman Kim, Euijoon Ahn, Dagan Feng
Dermoscopy images play an important role in the non-invasive early detection of melanoma [1].