11 research outputs found
MixBag: Bag-Level Data Augmentation for Learning from Label Proportions
Learning from label proportions (LLP) is a promising weakly supervised
learning problem. In LLP, a set of instances (bag) has label proportions, but
no instance-level labels are given. LLP aims to train an instance-level
classifier by using the label proportions of the bag. In this paper, we propose
a bag-level data augmentation method for LLP called MixBag, based on the key
observation from our preliminary experiments; that the instance-level
classification accuracy improves as the number of labeled bags increases even
though the total number of instances is fixed. We also propose a confidence
interval loss designed based on statistical theory to use the augmented bags
effectively. To the best of our knowledge, this is the first attempt to propose
bag-level data augmentation for LLP. The advantage of MixBag is that it can be
applied to instance-level data augmentation techniques and any LLP method that
uses the proportion loss. Experimental results demonstrate this advantage and
the effectiveness of our method.Comment: Accepted at ICCV202
Negative Pseudo Labeling Using Class Proportion for Semantic Segmentation in Pathology
16th European Conference, Glasgow, UK, August 23–28, 2020. Part of the Lecture Notes in Computer Science book series (LNCS, volume 12360). Also part of the Image Processing, Computer Vision, Pattern Recognition, and Graphics book sub series (LNIP, volume 12360).In pathological diagnosis, since the proportion of the adenocarcinoma subtypes is related to the recurrence rate and the survival time after surgery, the proportion of cancer subtypes for pathological images has been recorded as diagnostic information in some hospitals. In this paper, we propose a subtype segmentation method that uses such proportional labels as weakly supervised labels. If the estimated class rate is higher than that of the annotated class rate, we generate negative pseudo labels, which indicate, “input image does not belong to this negative label, ” in addition to standard pseudo labels. It can force out the low confidence samples and mitigate the problem of positive pseudo label learning which cannot label low confident unlabeled samples. Our method outperformed the state-of-the-art semi-supervised learning (SSL) methods
Easy Learning from Label Proportions
We consider the problem of Learning from Label Proportions (LLP), a weakly
supervised classification setup where instances are grouped into "bags", and
only the frequency of class labels at each bag is available. Albeit, the
objective of the learner is to achieve low task loss at an individual instance
level. Here we propose Easyllp: a flexible and simple-to-implement debiasing
approach based on aggregate labels, which operates on arbitrary loss functions.
Our technique allows us to accurately estimate the expected loss of an
arbitrary model at an individual level. We showcase the flexibility of our
approach by applying it to popular learning frameworks, like Empirical Risk
Minimization (ERM) and Stochastic Gradient Descent (SGD) with provable
guarantees on instance level performance. More concretely, we exhibit a
variance reduction technique that makes the quality of LLP learning deteriorate
only by a factor of k (k being bag size) in both ERM and SGD setups, as
compared to full supervision. Finally, we validate our theoretical results on
multiple datasets demonstrating our algorithm performs as well or better than
previous LLP approaches in spite of its simplicity
Hierarchical Neyman-Pearson Classification for Prioritizing Severe Disease Categories in COVID-19 Patient Data
COVID-19 has a spectrum of disease severity, ranging from asymptomatic to
requiring hospitalization. Understanding the mechanisms driving disease
severity is crucial for developing effective treatments and reducing mortality
rates. One way to gain such understanding is using a multi-class classification
framework, in which patients' biological features are used to predict patients'
severity classes. In this severity classification problem, it is beneficial to
prioritize the identification of more severe classes and control the
"under-classification" errors, in which patients are misclassified into less
severe categories. The Neyman-Pearson (NP) classification paradigm has been
developed to prioritize the designated type of error. However, current NP
procedures are either for binary classification or do not provide high
probability controls on the prioritized errors in multi-class classification.
Here, we propose a hierarchical NP (H-NP) framework and an umbrella algorithm
that generally adapts to popular classification methods and controls the
under-classification errors with high probability. On an integrated collection
of single-cell RNA-seq (scRNA-seq) datasets for 864 patients, we explore ways
of featurization and demonstrate the efficacy of the H-NP algorithm in
controlling the under-classification errors regardless of featurization. Beyond
COVID-19 severity classification, the H-NP algorithm generally applies to
multi-class classification problems, where classes have a priority order
Weakly supervised learning via statistical sufficiency
The Thesis introduces a novel algorithmic framework for
weakly supervised learn- ing, namely, for any any problem in
between supervised and unsupervised learning, from the labels
standpoint. Weak supervision is the reality in many applications
of machine learning where training is performed with partially
missing, aggregated- level and/or noisy labels. The approach is
grounded on the concept of statistical suf- ficiency and its
transposition to loss functions. Our solution is problem-agnostic
yet constructive as it boils down to a simple two-steps
procedure. First, estimate a suffi- cient statistic for the
labels from weak supervision. Second, plug the estimate into a
(newly defined) linear-odd loss function and learn the model by
any gradient-based solver, with a simple adaptation. We apply the
same approach to several challeng- ing learning problems: (i)
learning from label proportions, (ii) learning with noisy labels
for both linear classifiers and deep neural networks, and (iii)
learning from feature-wise distributed datasets where the entity
matching function is unknown