Search Results for author: Francesco Ciompi

Found 23 papers, 7 papers with code

Hitchhiker's guide to cancer-associated lymphoid aggregates in histology images: manual and deep learning-based quantification approaches

no code implementations6 Mar 2024 Karina Silina, Francesco Ciompi

Quantification of lymphoid aggregates including tertiary lymphoid structures with germinal centers in histology images of cancer is a promising approach for developing prognostic and predictive tissue biomarkers.

Decision Making

Uncertainty-guided annotation enhances segmentation with the human-in-the-loop

no code implementations16 Feb 2024 Nadieh Khalili, Joey Spronck, Francesco Ciompi, Jeroen van der Laak, Geert Litjens

Deep learning algorithms, often critiqued for their 'black box' nature, traditionally fall short in providing the necessary transparency for trusted clinical use.

Continual Learning

HoVer-UNet: Accelerating HoVerNet with UNet-based multi-class nuclei segmentation via knowledge distillation

1 code implementation21 Nov 2023 Cristian Tommasino, Cristiano Russo, Antonio Maria Rinaldi, Francesco Ciompi

We present HoVer-UNet, an approach to distill the knowledge of the multi-branch HoVerNet framework for nuclei instance segmentation and classification in histopathology.

Instance Segmentation Knowledge Distillation +1

LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset

no code implementations16 Jan 2023 Yiping Jiao, Jeroen van der Laak, Shadi Albarqouni, Zhang Li, Tao Tan, Abhir Bhalerao, Jiabo Ma, Jiamei Sun, Johnathan Pocock, Josien P. W. Pluim, Navid Alemi Koohbanani, Raja Muhammad Saad Bashir, Shan E Ahmed Raza, Sibo Liu, Simon Graham, Suzanne Wetstein, Syed Ali Khurram, Thomas Watson, Nasir Rajpoot, Mitko Veta, Francesco Ciompi

Additionally, we present post-competition results where we show how the presented methods perform on an independent set of lung cancer slides, which was not part of the initial competition, as well as a comparison on lymphocyte assessment between presented methods and a panel of pathologists.

Seeded iterative clustering for histology region identification

1 code implementation14 Nov 2022 Eduard Chelebian, Francesco Ciompi, Carolina Wählby

Annotations are necessary to develop computer vision algorithms for histopathology, but dense annotations at a high resolution are often time-consuming to make.

Clustering Segmentation +2

Quantifying the Scanner-Induced Domain Gap in Mitosis Detection

2 code implementations30 Mar 2021 Marc Aubreville, Christof Bertram, Mitko Veta, Robert Klopfleisch, Nikolas Stathonikos, Katharina Breininger, Natalie ter Hoeve, Francesco Ciompi, Andreas Maier

Hypothesizing that the scanner device plays a decisive role in this effect, we evaluated the susceptibility of a standard mitosis detection approach to the domain shift introduced by using a different whole slide scanner.

Mitosis Detection

HookNet: multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images

1 code implementation22 Jun 2020 Mart van Rijthoven, Maschenka Balkenhol, Karina Siliņa, Jeroen van der Laak, Francesco Ciompi

We propose HookNet, a semantic segmentation model for histopathology whole-slide images, which combines context and details via multiple branches of encoder-decoder convolutional neural networks.

Decoder Image Segmentation +4

Extending Unsupervised Neural Image Compression With Supervised Multitask Learning

no code implementations MIDL 2019 David Tellez, Diederik Hoppener, Cornelis Verhoef, Dirk Grunhagen, Pieter Nierop, Michal Drozdzal, Jeroen van der Laak, Francesco Ciompi

Additionally, we trained multiple encoders with different training objectives, e. g. unsupervised and variants of MTL, and observed a positive correlation between the number of tasks in MTL and the system performance on the TUPAC16 dataset.

Image Compression

Virtual staining for mitosis detection in Breast Histopathology

no code implementations17 Mar 2020 Caner Mercan, Germonda Reijnen-Mooij, David Tellez Martin, Johannes Lotz, Nick Weiss, Marcel van Gerven, Francesco Ciompi

We propose a virtual staining methodology based on Generative Adversarial Networks to map histopathology images of breast cancer tissue from H&E stain to PHH3 and vice versa.

Mitosis Detection

Needles in Haystacks: On Classifying Tiny Objects in Large Images

1 code implementation16 Aug 2019 Nick Pawlowski, Suvrat Bhooshan, Nicolas Ballas, Francesco Ciompi, Ben Glocker, Michal Drozdzal

In some important computer vision domains, such as medical or hyperspectral imaging, we care about the classification of tiny objects in large images.

Classification General Classification +2

Neural Image Compression for Gigapixel Histopathology Image Analysis

1 code implementation7 Nov 2018 David Tellez, Geert Litjens, Jeroen van der Laak, Francesco Ciompi

Second, a convolutional neural network (CNN) is trained on these compressed image representations to predict image-level labels, avoiding the need for fine-grained manual annotations.

Image Compression Medical Diagnosis

Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks

no code implementations17 Aug 2018 David Tellez, Maschenka Balkenhol, Irene Otte-Holler, Rob van de Loo, Rob Vogels, Peter Bult, Carla Wauters, Willem Vreuls, Suzanne Mol, Nico Karssemeijer, Geert Litjens, Jeroen van der Laak, Francesco Ciompi

Application of CNNs to hematoxylin and eosin (H&E) stained histological tissue sections is hampered by: (1) noisy and expensive reference standards established by pathologists, (2) lack of generalization due to staining variation across laboratories, and (3) high computational requirements needed to process gigapixel whole-slide images (WSIs).

Data Augmentation Knowledge Distillation +2

The importance of stain normalization in colorectal tissue classification with convolutional networks

1 code implementation20 Feb 2017 Francesco Ciompi, Oscar Geessink, Babak Ehteshami Bejnordi, Gabriel Silva de Souza, Alexi Baidoshvili, Geert Litjens, Bram van Ginneken, Iris Nagtegaal, Jeroen van der Laak

The development of reliable imaging biomarkers for the analysis of colorectal cancer (CRC) in hematoxylin and eosin (H&E) stained histopathology images requires an accurate and reproducible classification of the main tissue components in the image.

Classification General Classification

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