Expected exponential loss for gaze-based video and volume ground truth annotation

Many recent machine learning approaches used in medical imaging are highly reliant on large amounts of image and ground truth data. In the context of object segmentation, pixel-wise annotations are extremely expensive to collect, especially in video and 3D volumes. To reduce this annotation burden, we propose a novel framework to allow annotators to simply observe the object to segment and record where they have looked at with a \$200 eye gaze tracker. Our method then estimates pixel-wise probabilities for the presence of the object throughout the sequence from which we train a classifier in semi-supervised setting using a novel Expected Exponential loss function. We show that our framework provides superior performances on a wide range of medical image settings compared to existing strategies and that our method can be combined with current crowd-sourcing paradigms as well.Comment: 9 pages, 5 figues, MICCAI 2017 - LABELS Worksho

Crowd disagreement about medical images is informative

Classifiers for medical image analysis are often trained with a single consensus label, based on combining labels given by experts or crowds. However, disagreement between annotators may be informative, and thus removing it may not be the best strategy. As a proof of concept, we predict whether a skin lesion from the ISIC 2017 dataset is a melanoma or not, based on crowd annotations of visual characteristics of that lesion. We compare using the mean annotations, illustrating consensus, to standard deviations and other distribution moments, illustrating disagreement. We show that the mean annotations perform best, but that the disagreement measures are still informative. We also make the crowd annotations used in this paper available at \url{https://figshare.com/s/5cbbce14647b66286544}.Comment: Accepted for publication at MICCAI LABELS 201

PENGARUH BERBAGAI RASIO RUMPUT LAPANG FERMENTASI DAN KONSENTRAT TERHADAP KECERNAAN NDF DAN ADF DOMBA EKOR TIPIS

Penelitian ini bertujuan untuk mengetahui pengaruh pemberian berbagai rasio rumput lapang fermentasi dan konsentrat dalam ransum terhadap konsumsi serta kecernaan Neutral Detergent Fiber (NDF) dan Acid Detergent Fiber (ADF) domba ekor tipis jantan. Materi penelitian berupa domba ekor tipis jantan sebanyak 15 ekor yang berumur sekitar 11–15 bulan dengan rata-rata bobot badan awal 25,4±3,65 kg dan bahan pakan yang terdiri dari rumput lapang fermentasi dan konsentrat. Desain penelitian ini menggunakan rancangan acak kelompok dengan tiga macam perlakuan dan lima kelompok bobot badan sebagai ulangan. Setiap ulangan terdiri dari satu ekor domba ekor tipis jantan. Perlakuan dalam ransum terdiri dari P0= 30% RLF + 70% konsentrat, P1= 50% RLF + 50% konsentrat dan P2= 70% RLF + 30% konsentrat. Peubah yang diamati adalah konsumsi NDF, konsumsi ADF, kecernaan NDF dan kecernaan ADF. Data yang diperoleh dianalisis menggunakan analisis variansi untuk mengetahui adanya pengaruh perlakuan terhadap peubah yang diamati. Hasil analisis variansi menunujukkan bahwa pemberian rumput lapang dan konsentrat dalam berbagai rasio tidak berpengaruh terhadap konsumsi NDF, konsumsi ADF, kecernaan NDF dan kecernaan ADF domba ekor tipis. Kesimpulan yang dapat diambil dari penilitian ini adalah konsumsi dan kecernaan NDF serta ADF pada penggunaan rumput lapang fermentasi dan konsentrat rasio 70:30% relatif sama dengan rasio 30:70%. Kata kunci: Domba ekor tipis, Rumput lapang fermentasi, NDF, AD

Efficient multi-class fetal brain segmentation in high resolution MRI reconstructions with noisy labels

Segmentation of the developing fetal brain is an important step in quantitative analyses. However, manual segmentation is a very time-consuming task which is prone to error and must be completed by highly specialized indi-viduals. Super-resolution reconstruction of fetal MRI has become standard for processing such data as it improves image quality and resolution. However, dif-ferent pipelines result in slightly different outputs, further complicating the gen-eralization of segmentation methods aiming to segment super-resolution data. Therefore, we propose using transfer learning with noisy multi-class labels to automatically segment high resolution fetal brain MRIs using a single set of seg-mentations created with one reconstruction method and tested for generalizability across other reconstruction methods. Our results show that the network can auto-matically segment fetal brain reconstructions into 7 different tissue types, regard-less of reconstruction method used. Transfer learning offers some advantages when compared to training without pre-initialized weights, but the network trained on clean labels had more accurate segmentations overall. No additional manual segmentations were required. Therefore, the proposed network has the potential to eliminate the need for manual segmentations needed in quantitative analyses of the fetal brain independent of reconstruction method used, offering an unbiased way to quantify normal and pathological neurodevelopment.Comment: Accepted for publication at PIPPI MICCAI 202

Why is the Winner the Best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work

Cats or CAT scans: transfer learning from natural or medical image source data sets?

Transfer learning is a widely used strategy in medical image analysis. Instead of only training a network with a limited amount of data from the target task of interest, we can first train the network with other, potentially larger source data sets, creating a more robust model. The source data sets do not have to be related to the target task. For a classification task in lung computed tomography (CT) images, we could use both head CT images and images of cats as the source. While head CT images appear more similar to lung CT images, the number and diversity of cat images might lead to a better model overall. In this survey, we review a number of articles that have studied similar comparisons. Although the answer to which strategy is best seems to be ‘it depends’, we discuss a number of research directions we need to take as a community to gain more understanding of this topic

Random Subspace Method for One-Class Classifiers

Pattern Recognition and BioinformaticsElectrical Engineering, Mathematics and Computer Scienc

Dissimilarity-Based Multiple Instance Learning

Multiple instance learning (MIL) is an extension of supervised learning where the objects are represented by sets (bags) of feature vectors (instances) rather than individual feature vectors. For example, an image can be represented by a bag of instances, where each instance is a patch in that image. Only bag labels are given, however, the standard assumption is that that a bag is positive if and only if it contains a positive, or concept instance. In other words, only concept instances are informative for the bag label. The goal is to learn a bag classifier, although an instance classifier may also be desired. This scenario is suitable for applications where objects are heterogeneous and representing them as a single feature vector may lose important information, and/or in cases where only weakly labeled data is available. Several approaches to MIL exist. Instance-based approaches rely on stronger assumptions about the relationship of the instance labels and the bag labels, and define a bag classifier through an instance classifier. Bag-based approaches learn a bag classifier directly, often by converting the problem into a supervised problem. These methods often disregard the standard assumption, and instead use the collective assumption, where all instances are informative. One way to convert the problem into a supervised one, is to describe each bag by a vector of its distances to a set of reference prototypes. In this so-called dissimilarity representation, supervised classifiers can be used. The goal of this thesis is to study the dissimilarity representation as a method for dealing with multiple instance learning problems. We address the questions of defining a dissimilarity function and choosing a reference set of prototypes, while considering the assumptions that these choices implicitly make about the problem.Intelligent SystemsElectrical Engineering, Mathematics and Computer Scienc

Crowd disagreement of medical images is informative

\u3cp\u3eClassifiers for medical image analysis are often trained with a single consensus label, based on combining labels given by experts or crowds. However, disagreement between annotators may be informative, and thus removing it may not be the best strategy. As a proof of concept, we predict whether a skin lesion from the ISIC 2017 dataset is a melanoma or not, based on crowd annotations of visual characteristics of that lesion. We compare using the mean annotations, illustrating consensus, to standard deviations and other distribution moments, illustrating disagreement. We show that the mean annotations perform best, but that the disagreement measures are still informative. We also make the crowd annotations used in this paper available at https://figshare.com/s/5cbbce14647b66286544.\u3c/p\u3