Content-Based Image Retrieval
The 7th edition of the Valais/Wallis AI Workshop will take place on Nov. 15 2021 at the IDIAP research institute with a live youtube streaming (hybrid event).
Organized by IDIAP and HES-SO Valais, it will feature various presentations and discussions on “Energy in all its artificial states”, including a keynote presentation by Pr Guglielmina Mutani.
Full program and registration are now available online: https://www.idiap.ch/workshop/valais-wallis-ai-workshop/program
The proceedings of the Conference and Labs of the Evaluation Forum (CLEF) 2021 conference are now available online: https://link.springer.com/book/10.1007%2F978-3-030-85251-1
It includes various interesting papers on information retrieval as well as overview reports of the different labs including ImageCLEF and LifeCLEF.
We will be presenting our two papers on Head and Neck tumor segmentation and prognostic prediction, and one paper on colorectal cancer detection in whole slide images at MICCAI workshops Sept. 27th and Oct. 1st. Together with our paper at the main conference and the organization of the HECKTOR challenge, we are happy to have a total of five great contributions to MICCAI this year.
Our review paper on harmonization methods in radiomics, in collaboration with the School for Oncology, Maastricht University, has been published in the Journal of Personalized Medicine.
The paper is now available online: “Making Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization Methods“, Shruti Atul Mali et al.
Radiomics aims to extract quantitative features from medical images to improve decision support. In this work, we discuss various harmonization solutions to make the radiomic features more reproducible across various scanners and protocol settings. The different harmonization solutions are divided into two main categories: image domain (including image acquisition, post-processing of raw sensor-level image data, data augmentation, and style transfer) and feature domain (including statistical normalization, intensity harmonization, ComBat and deep learning harmonization).
Our paper on “Multi_Scale_Tools: A Python Library to Exploit Multi-Scale Whole Slide Images” by Niccolò Marini et al. has been published in Frontiers in Computer Science, section Digital Public Health.
This paper describes our python library called Multi_Scale_Tools. It includes four components:
The code is available on GitHub: https://github.com/sara-nl/multi-scale-tools
The emulation of the HECKTOR challenge at MICCAI2021 is on with many active participants! The test data has just been released and the submission website will be open from Sep. 01 to Sep. 10. We are very much looking forward to your participation !
More info here: https://www.aicrowd.com/challenges/miccai-2021-hecktor
We are very grateful to the HECKTOR 2021 sponsors:
Task 1 (segmentation) is sponsored by Siemens Healthineers Switzerland with a prize of 500 €.
Task 2 (prediction of PFS) is sponsored by Aquilab with a prize of 500 €.
Task 3 (prediction of PFS using ground-truth delineation of tumors) is sponsored by Biomemtech with a prize of 500 €.
Our paper “Sharpening Local Interpretable Model-agnostic Explanations for Histopathology: Improved Understandability and Reliability” by Mara Graziani, Iam Palatnik De Sousa et. al will be presented at MICCAI 2021, Sept. 27 to Oct 1st, Strasbourg, France.
In this work, we improve the application of LIME to histopathology images by leveraging nuclei annotations. The obtained visualizations reveal the sharp, neat and high attention of the deep classifier to the neoplastic nuclei in the dataset, an observation in line with clinical decision making.
Compared to standard LIME, our explanations show improved understandability for domain-experts, report higher stability and pass the sanity checks of consistency to data or initialization changes and sensitivity to network parameters.
Congratulations Dr. Otálora for this well-deserved achievement!
Sebastian successfully defended his PhD thesis entitled “Deep Learning for Histopathology Image Analysis from Heterogeneous & Multimodal Data Sources” supervised by Prof. Stephane Marchand-Maillet and, Prof. Henning Müller.
Our work on “The Discriminative Power and Stability of Radiomics Features With Computed Tomography Variations“, by Oscar Jimenez-del-Toro et al. has been published in Investigative Radiology.
Using a 3D-printed CT phantom based on real patient data, we studied the stability of radiomics features against CT parameter variations and their discriminative power for tissue classification.
Our work on “Common limitations of performance metrics in biomedical image analysis” has been accepted for publication in MIDL 2021. Conducted by a large international consortium including the BIAS initiative, the MICCAI Society’s challenge working group and the benchmarking working group of the MONAI framework, this initiative aims at generating best practice recommendations with respect to metrics in medical image analysis.
Full paper available on arxiv: https://arxiv.org/abs/2104.05642
Check out our steerable detectors if you lost your (biological) patterns in noisy backgrounds 🙂 https://ieeexplore.ieee.org/document/9406375
A single-shot template learning will allow finding all occurrences no matter their orientation are and with high recall and specificity !
And it is obviously all packaged in an open-source ImageJ plugin!
http://bigwww.epfl.ch/algorithms/steer_n_detect
Plus open source code on GitHub:
https://github.com/Biomedical-Imaging-Group/Steer-n-Detect
Joint work with Julien Fageot, Virginie Uhlmann, Zsuzsanna Püspöki, Benjamin Beck and Michael Unser.
The second edition of the HECKTOR challenge will take place at MICCAI 2021 tackling the Head & Neck tumor analysis in PET-CT images. Visit the challenge page for full description: https://www.aicrowd.com/challenges/miccai-2021-hecktor
In addition to last year’s challenge, we will include more data for the segmentation task as well as a new task for the automatic prediction of patient outcome.
Evaluate your cutting edge algorithm on 300 cases with high-quality annotations from six centers.
Timeline:
Our work “On the Scale Invariance in State of the Art CNNs Trained on ImageNet”, by Mara Graziani, Thomas Lompech, Henning Müller, Adrien Depeursinge and Vincent Andrearczyk. was published with open access rights in a special issue of the Machine Learning and Knowledge Extraction (MAKE) journal: https://www.mdpi.com/2504-4990/3/2/19#abstractc
By using our tool Regression Concept Vectors (pip install rcvtool), we modeled information about scale within intermediate CNN layers. Scale covariance peaks at deep layers and invariance is learned only in the final layers.
Based on this, we designed a pruning strategy that preserves scale-covariant features. This gives better transfer learning results for medical tasks where scale is discriminative, for example, to distinguish the magnification level of microscope images of tissue biopses.
The second edition of the HECKTOR challenge was accepted at MICCAI 2021. This challenge is designed for researchers in medical imaging to compare their algorithms for automatic segmentation of tumor in Head and Neck Cancer. In addition to last year’s challenge, we will include more data for the segmentation task as well as a new task for the automatic prediction of patient outcome.
Timeline:
More information will come soon !
This week took place the kickoff meeting of BIGPICTURE, an EU project gathering about 50 partners including hospitals, research centers and pharmaceutical companies with the aim to create the largest database of histopathology images for computer assisted cancer diagnostic and treatment planning.
Our abstract entitled ‘Assessment of the stability and discriminative power of radiomics features in liver lesions using an anthropomorphic 3D-printed CT phantom’ by Jimenez-del-Toro et al. has been accepted to be presented at the Swiss Congress of Radiology 2021 ONLINE edition, as an Oral Presentation.
Our abstract entitled ‘Revealing the most suitable CT radiomics features for retrospective studies with heterogeneous datasets’ by Jimenez-del-Toro et al. has been accepted to be presented at the ONLINE European Congress of Radiology 2021, for the session ‘AI in abdominal imaging’, held from March 3-7, 2021.
Our paper on “The development of attentional control mechanisms in multisensory environments” has been accepted for publication in Developmental Cognitive Neuroscience, Elsevier.
This work led by Nora Turoman, Pawel J. Matusz and their colleagues focused on using advanced multivariate EEG analyses to understand how children develop their attentional control in real-world like settings and when different processes that underlie this attention control reach the adult-like state.
Deep learning models may hide inherent risks: codification of biases, weak accountability and little transparency of the decision-making.
Our workshop at AMLD2021 (Building Interpretable AI for Digital Pathology) offers an all-round discussion on building interpretable AI for digital pathology.
Join us to hear about visualization methods, concept attribution and interpretable graph-networks.
Our team, led by Prof. Henning Müller, started a collaboration with the American College of Radiology to develop AI tools that help radiologists in their medical decision making. Le Nouvelliste wrote an article about this collaboration (in French).
