no code implementations • 18 Jan 2024 • Negar Golestani, Aihui Wang, Gregory R Bean, Mirabela Rusu
Training such automated detection models require accurate ground truth labels on ex-vivo radiology images, which can be acquired through registering Faxitron and histopathology images and mapping the extent of cancer from histopathology to x-ray images.
no code implementations • 8 Dec 2023 • Sulaiman Vesal, Indrani Bhattacharya, Hassan Jahanandish, Xinran Li, Zachary Kornberg, Steve Ran Zhou, Elijah Richard Sommer, Moon Hyung Choi, Richard E. Fan, Geoffrey A. Sonn, Mirabela Rusu
We trained and validated ProsDectNet using a cohort of 289 patients who underwent MRI-TRUS fusion targeted biopsy.
no code implementations • 29 Sep 2022 • Karin Stacke, Indrani Bhattacharya, Justin R. Tse, James D. Brooks, Geoffrey A. Sonn, Mirabela Rusu
CorrFABR consists of three main steps: (1) Feature Aggregation where region-level features are extracted from radiology and pathology images, (2) Fusion where radiology features correlated with pathology features are learned on a region level, and (3) Prediction where the learned correlated features are used to distinguish aggressive from indolent clear cell RCC using CT alone as input.
1 code implementation • 5 Sep 2022 • Sulaiman Vesal, Iani Gayo, Indrani Bhattacharya, Shyam Natarajan, Leonard S. Marks, Dean C Barratt, Richard E. Fan, Yipeng Hu, Geoffrey A. Sonn, Mirabela Rusu
We trained our model on 764 subjects from one institution and finetuned our model using only ten subjects from subsequent institutions.
no code implementations • 13 Jul 2022 • Ziyi Shen, Qianye Yang, Yuming Shen, Francesco Giganti, Vasilis Stavrinides, Richard Fan, Caroline Moore, Mirabela Rusu, Geoffrey Sonn, Philip Torr, Dean Barratt, Yipeng Hu
Image registration is useful for quantifying morphological changes in longitudinal MR images from prostate cancer patients.
no code implementations • 8 Jun 2022 • Mingxuan Gu, Sulaiman Vesal, Mareike Thies, Zhaoya Pan, Fabian Wagner, Mirabela Rusu, Andreas Maier, Ronak Kosti
Then, to align the source and target features and tackle the memory issue of the traditional contrastive loss, we propose the centroid-based contrastive learning (CCL) and a centroid norm regularizer (CNR) to optimize the contrastive pairs in both direction and magnitude.
1 code implementation • 27 Mar 2022 • Shaheer U. Saeed, Yunguan Fu, Vasilis Stavrinides, Zachary M. C. Baum, Qianye Yang, Mirabela Rusu, Richard E. Fan, Geoffrey A. Sonn, J. Alison Noble, Dean C. Barratt, Yipeng Hu
In this paper, we consider image quality assessment (IQA) as a measure of how images are amenable with respect to a given downstream task, or task amenability.
1 code implementation • 20 Feb 2022 • Shaheer U. Saeed, Wen Yan, Yunguan Fu, Francesco Giganti, Qianye Yang, Zachary M. C. Baum, Mirabela Rusu, Richard E. Fan, Geoffrey A. Sonn, Mark Emberton, Dean C. Barratt, Yipeng Hu
This allows for the trained IQA controller to measure the impact an image has on the target task performance, when this task is performed using the predictor, e. g. segmentation and classification neural networks in modern clinical applications.
no code implementations • 8 Dec 2021 • Alessa Hering, Lasse Hansen, Tony C. W. Mok, Albert C. S. Chung, Hanna Siebert, Stephanie Häger, Annkristin Lange, Sven Kuckertz, Stefan Heldmann, Wei Shao, Sulaiman Vesal, Mirabela Rusu, Geoffrey Sonn, Théo Estienne, Maria Vakalopoulou, Luyi Han, Yunzhi Huang, Pew-Thian Yap, Mikael Brudfors, Yaël Balbastre, Samuel Joutard, Marc Modat, Gal Lifshitz, Dan Raviv, Jinxin Lv, Qiang Li, Vincent Jaouen, Dimitris Visvikis, Constance Fourcade, Mathieu Rubeaux, Wentao Pan, Zhe Xu, Bailiang Jian, Francesca De Benetti, Marek Wodzinski, Niklas Gunnarsson, Jens Sjölund, Daniel Grzech, Huaqi Qiu, Zeju Li, Alexander Thorley, Jinming Duan, Christoph Großbröhmer, Andrew Hoopes, Ingerid Reinertsen, Yiming Xiao, Bennett Landman, Yuankai Huo, Keelin Murphy, Nikolas Lessmann, Bram van Ginneken, Adrian V. Dalca, Mattias P. Heinrich
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed.
no code implementations • 3 Dec 2021 • Indrani Bhattacharya, David S. Lim, Han Lin Aung, Xingchen Liu, Arun Seetharaman, Christian A. Kunder, Wei Shao, Simon J. C. Soerensen, Richard E. Fan, Pejman Ghanouni, Katherine J. To'o, James D. Brooks, Geoffrey A. Sonn, Mirabela Rusu
Our experiments show that (1) radiologist labels and models trained with them can miss cancers, or underestimate cancer extent, (2) digital pathologist labels and models trained with them have high concordance with pathologist labels, and (3) models trained with digital pathologist labels achieve the best performance in prostate cancer detection in two different cohorts with different disease distributions, irrespective of the model architecture used.
1 code implementation • 31 Jul 2021 • Shaheer U. Saeed, Yunguan Fu, Vasilis Stavrinides, Zachary M. C. Baum, Qianye Yang, Mirabela Rusu, Richard E. Fan, Geoffrey A. Sonn, J. Alison Noble, Dean C. Barratt, Yipeng Hu
Using 6644 clinical ultrasound images from 249 prostate cancer patients, our results for image classification and segmentation tasks show that the proposed IQA method can be adapted using data with as few as respective 19. 7% and 29. 6% expert-reviewed consensus labels and still achieve comparable IQA and task performance, which would otherwise require a training dataset with 100% expert labels.
no code implementations • 23 Jun 2021 • Wei Shao, Indrani Bhattacharya, Simon J. C. Soerensen, Christian A. Kunder, Jeffrey B. Wang, Richard E. Fan, Pejman Ghanouni, James D. Brooks, Geoffrey A. Sonn, Mirabela Rusu
Cancer labels achieved by image registration can be used to improve radiologists' interpretation of MRI by training deep learning models for early detection of prostate cancer.
1 code implementation • 12 Jun 2021 • Wei Shao, Yue Pan, Oguz C. Durumeric, Joseph M. Reinhardt, John E. Bayouth, Mirabela Rusu, Gary E. Christensen
Correspondences are estimated by accounting for the local tissue density change associated with air entering and leaving the lungs, and using binary artifact masks to exclude regions with artifacts from image regression.
no code implementations • 15 Feb 2021 • Shaheer U. Saeed, Yunguan Fu, Zachary M. C. Baum, Qianye Yang, Mirabela Rusu, Richard E. Fan, Geoffrey A. Sonn, Dean C. Barratt, Yipeng Hu
In this paper, we consider a type of image quality assessment as a task-specific measurement, which can be used to select images that are more amenable to a given target task, such as image classification or segmentation.
1 code implementation • Medical Image Analysis 2021 • Rewa R. Sood, Wei Shao, Christian Kunder, Nikola C. Teslovich, Jeffrey B. Wang, Simon J.C. Soerensen, Nikhil Madhuripan, Anugayathri Jawahar, James D. Brooks, Pejman Ghanouni, Richard E. Fan, Geoffrey A. Sonn, Mirabela Rusu
Improvement in cancer detection on MRI is essential to reducing this variability and maximizing the clinical utility of MRI.
no code implementations • 31 Jul 2020 • Indrani Bhattacharya, Arun Seetharaman, Wei Shao, Rewa Sood, Christian A. Kunder, Richard E. Fan, Simon John Christoph Soerensen, Jeffrey B. Wang, Pejman Ghanouni, Nikola C. Teslovich, James D. Brooks, Geoffrey A. Sonn, Mirabela Rusu
First, the model learns MRI signatures of cancer that are correlated with corresponding histopathology features using Common Representation Learning.
no code implementations • 19 Dec 2019 • Rewa Sood, Binit Topiwala, Karthik Choutagunta, Rohit Sood, Mirabela Rusu
Acquiring High Resolution (HR) Magnetic Resonance (MR) images requires the patient to remain still for long periods of time, which causes patient discomfort and increases the probability of motion induced image artifacts.
no code implementations • 19 Dec 2019 • Rewa Sood, Mirabela Rusu
Acquiring High Resolution (HR) Magnetic Resonance (MR) images requires the patient to remain still for long periods of time, which causes patient discomfort and increases the probability of motion induced image artifacts.