Search Results for author: Anne L. Martel

Found 17 papers, 8 papers with code

Metastatic Cancer Outcome Prediction with Injective Multiple Instance Pooling

no code implementations9 Mar 2022 Jianan Chen, Anne L. Martel

Cancer stage is a large determinant of patient prognosis and management in many cancer types, and is often assessed using medical imaging modalities, such as CT and MRI.

Multiple Instance Learning

BI-RADS BERT & Using Section Segmentation to Understand Radiology Reports

1 code implementation14 Oct 2021 Grey Kuling, Dr. Belinda Curpen, Anne L. Martel

In this work, we pre-trained a contextual embedding BERT model using breast radiology reports and developed a classifier that incorporated the embedding with auxiliary global textual features in order to perform section segmentation.

Natural Language Processing Word Embeddings

Resource and data efficient self supervised learning

no code implementations3 Sep 2021 Ozan Ciga, Tony Xu, Anne L. Martel

We investigate the utility of pretraining by contrastive self supervised learning on both natural-scene and medical imaging datasets when the unlabeled dataset size is small, or when the diversity within the unlabeled set does not lead to better representations.

Self-Supervised Learning

Common Limitations of Image Processing Metrics: A Picture Story

1 code implementation12 Apr 2021 Annika Reinke, Minu D. Tizabi, Carole H. Sudre, Matthias Eisenmann, Tim Rädsch, Michael Baumgartner, Laura Acion, Michela Antonelli, Tal Arbel, Spyridon Bakas, Peter Bankhead, Arriel Benis, M. Jorge Cardoso, Veronika Cheplygina, Evangelia Christodoulou, Beth Cimini, Gary S. Collins, Keyvan Farahani, Bram van Ginneken, Ben Glocker, Patrick Godau, Fred Hamprecht, Daniel A. Hashimoto, Doreen Heckmann-Nötzel, Michael M. Hoffmann, Merel Huisman, Fabian Isensee, Pierre Jannin, Charles E. Kahn, Alexandros Karargyris, Alan Karthikesalingam, Bernhard Kainz, Emre Kavur, Hannes Kenngott, Jens Kleesiek, Thijs Kooi, Michal Kozubek, Anna Kreshuk, Tahsin Kurc, Bennett A. Landman, Geert Litjens, Amin Madani, Klaus Maier-Hein, Anne L. Martel, Peter Mattson, Erik Meijering, Bjoern Menze, David Moher, Karel G. M. Moons, Henning Müller, Brennan Nichyporuk, Felix Nickel, Jens Petersen, Gorkem Polat, Nasir Rajpoot, Mauricio Reyes, Nicola Rieke, Michael Riegler, Hassan Rivaz, Julio Saez-Rodriguez, Clarisa Sanchez Gutierrez, Julien Schroeter, Anindo Saha, Shravya Shetty, Maarten van Smeden, Bram Stieltjes, Ronald M. Summers, Abdel A. Taha, Sotirios A. Tsaftaris, Ben van Calster, Gaël Varoquaux, Manuel Wiesenfarth, Ziv R. Yaniv, Annette Kopp-Schneider, Paul Jäger, Lena Maier-Hein

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation.

Instance Segmentation object-detection +2

Self-supervised driven consistency training for annotation efficient histopathology image analysis

2 code implementations7 Feb 2021 Chetan L. Srinidhi, Seung Wook Kim, Fu-Der Chen, Anne L. Martel

In this work, we overcome this challenge by leveraging both task-agnostic and task-specific unlabeled data based on two novel strategies: i) a self-supervised pretext task that harnesses the underlying multi-resolution contextual cues in histology whole-slide images to learn a powerful supervisory signal for unsupervised representation learning; ii) a new teacher-student semi-supervised consistency paradigm that learns to effectively transfer the pretrained representations to downstream tasks based on prediction consistency with the task-specific un-labeled data.

Histopathological Image Classification Representation Learning +1

AMINN: Autoencoder-based Multiple Instance Neural Network Improves Outcome Prediction of Multifocal Liver Metastases

1 code implementation12 Dec 2020 Jianan Chen, Helen M. C. Cheung, Laurent Milot, Anne L. Martel

Experimental results empirically validated our hypothesis that incorporating imaging features of all lesions improves outcome prediction for multifocal cancer.

Overcoming the limitations of patch-based learning to detect cancer in whole slide images

no code implementations1 Dec 2020 Ozan Ciga, Tony Xu, Sharon Nofech-Mozes, Shawna Noy, Fang-I Lu, Anne L. Martel

We apply a binary cancer detection network on post neoadjuvant therapy breast cancer WSIs to find the tumor bed outlining the extent of cancer, a task which requires sensitivity and precision across the whole slide.

whole slide images

Self supervised contrastive learning for digital histopathology

1 code implementation27 Nov 2020 Ozan Ciga, Tony Xu, Anne L. Martel

In this paper, we use a contrastive self-supervised learning method called SimCLR that achieved state-of-the-art results on natural-scene images and apply this method to digital histopathology by collecting and pretraining on 57 histopathology datasets without any labels.

Contrastive Learning Self-Supervised Learning

Deep neural network models for computational histopathology: A survey

no code implementations28 Dec 2019 Chetan L. Srinidhi, Ozan Ciga, Anne L. Martel

Histopathological images contain rich phenotypic information that can be used to monitor underlying mechanisms contributing to diseases progression and patient survival outcomes.

Transfer Learning

Learning to segment images with classification labels

1 code implementation28 Dec 2019 Ozan Ciga, Anne L. Martel

Annotating data for segmentation is generally considered to be more laborious as the annotator has to draw around the boundaries of regions of interest, as opposed to assigning image patches a class label.

Classification General Classification +1

Unsupervised Clustering of Quantitative Imaging Phenotypes using Autoencoder and Gaussian Mixture Model

no code implementations6 Sep 2019 Jianan Chen, Laurent Milot, Helen M. C. Cheung, Anne L. Martel

The performance of the proposed pipeline was evaluated on an institutional MRI cohort of 108 patients with colorectal cancer liver metastases.

Representation Learning

Intensity augmentation for domain transfer of whole breast segmentation in MRI

no code implementations5 Sep 2019 Linde S. Hesse, Grey Kuling, Mitko Veta, Anne L. Martel

Our results show that using intensity augmentation in addition to geometric augmentation is a suitable method to overcome the intensity domain shift and we expect it to be useful for a wide range of segmentation tasks.

Domain Adaptation Style Transfer

Cluster-Based Learning from Weakly Labeled Bags in Digital Pathology

1 code implementation28 Nov 2018 Shazia Akbar, Anne L. Martel

To alleviate the burden of gathering detailed expert annotations when training deep neural networks, we propose a weakly supervised learning approach to recognize metastases in microscopic images of breast lymph nodes.

Interpreting extracted rules from ensemble of trees: Application to computer-aided diagnosis of breast MRI

no code implementations27 Jun 2016 Cristina Gallego-Ortiz, Anne L. Martel

High predictive performance and ease of use and interpretability are important requirements for the applicability of a computer-aided diagnosis (CAD) to human reading studies.

General Classification Lesion Classification

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