Challenges will take place on Friday, April 3.
Challenge 1: White Matter Microstructure with Diffusion MRI Challenge
Bennett A. Landman: firstname.lastname@example.org
Kurt G. Schilling: email@example.com
Challenge 2: Multi-organ Nuclei Segmentation And Classification Challenge 2020
Abstract: In this challenge, participants will be provided with H&E stained tissue images of four organs with annotations of multiple cell-types including epithelial cells, lymphocytes, macrophages, and neutrophils. Participants will use the annotated dataset to develop computer vision algorithms to recognize these cell-types from the tissue images of unseen patients released in the testing set of the challenge. Additionally, all cell-types will not have equal number of annotated instances in the training dataset which will encourage the participants to develop algorithms for learning from imbalanced classes in a few shot learning paradigm.
Ruchika Verma: firstname.lastname@example.org
Challenge 3: Endoscopy vision challenge on segmentation and detection
Abstract: Smart endoscopy requires automated solutions to tackle inevitable artefacts such as motion blur, organ specularities, illumination variabilities and sensor artefacts. Once the fundamental artefact problem is addressed, it is also of eminent interest to detect automatically anomalies present in the organ under surveillance. EndoCV2020 challenge thus introduces two core sub-themes in endoscopy: 1) artefact detection and segmentation (EAD2020) and 2) disease detection and segmentation (EDD2020).
Sharib Ali: email@example.com
Felix Zhou: firstname.lastname@example.org
Konstantin Pogorelov: email@example.com
Challenge 4: AccelMR 2020
Awais Mansoor: firstname.lastname@example.org
Challenge 5: Diabetic Retinopathy Assessment Grading and Diagnosis
Abstract: Automated machine learning can facilitate the early diagnosis and timely treatment of diabetic retinopathy. Following the 1st Diabetic Retinopathy: Segmentation and Grading Challenge held with ISBI in 2018, we would like to promote the progress further through 2nd challenge using a new dataset, Deep Diabetic Retinopathy Image Dataset (DeepDRiD). The challenge is subdivided into three tasks as follows:
A. Dual-View Disease Grading: Classification of fundus images according to the severity level of diabetic retinopathy using dual view retinal fundus images.
B. Image Quality Estimation: Fundus quality assessment for overall image quality, artifacts, clarity, and field definition.
C. Transfer Learning: Explore the generalizability of a Diabetic Retinopathy (DR) grading system. The robust and generalizable models are expected to be developed to solve clinical issues in reality.
Hongyu Kong: email@example.com
Ruogu Fang: firstname.lastname@example.org
Prasanna Porwal: email@example.com
Challenge 6: 5th ISBI Cell Tracking Challenge
Abstract: The Cell Tracking Challenge is a well-established competition, with the primary aim of objectively benchmarking state-of-the-art cell segmentation and tracking methods over a diverse and annotated repository of multidimensional time-lapse image data of cells and nuclei captured using different light microscopy modalities. In its fifth edition, the scope of the challenge will be broadened by adding two datasets from a previously not covered modality of bright-field microscopy and one difficult dataset of developing Tribolium Castaneum embryo (full 3D+time videos as compared to their 3D cartographic projections released in the fourth edition last year). Finally, a silver segmentation ground truth corpus will be released for the training videos of nine previously existing datasets to facilitate the tuning of competing methods.
Carlos Ortiz de Solórzano: firstname.lastname@example.org
Michal Kozubek: email@example.com
Challenge 7: Automatic Detection challenge on Age-related Macular degeneration
Abstract: Age-related macular degeneration, abbreviated as AMD, is a degenerative disorder in the macular region. It mainly occurs in people older than 45 years old and its incidence rate is even higher than diabetic retinopathy in the elderly. Early diagnosis of AMD is crucial to treatment and prognosis. Fundus photo is one of the basic examinations. This dataset is composed of AMD and non-AMD (myopia, normal control, etc.) photos. Typical signs of AMD that can be found in these photos include drusen, exudation, hemorrhage, etc.
Yanwu Xu: firstname.lastname@example.org