Kernel-Based Stochastic Learning of Large-Scale Semiparametric Monotone Index Models with an Application to Aging and Household Risk Preference

This paper studies semiparametric estimation of monotone index models in a data-rich environment, where the number of covariates ($p$) and sample size ($n$) can both be large. Motivated by the mini-batch gradient descent algorithm (MBGD) that is widely used as a stochastic optimization tool in the machine learning field, this paper proposes a novel subsample- and iteration-based semiparametric estimation procedure. Starting from any initial guess of the parameter, in each round of iteration we draw a random subsample from the data set, and use such subsample to update the parameter based on the gradient of some well-chosen loss function, where the nonparametric component is replaced with its kernel estimator. Our proposed algorithm essentially generalizes MBGD algorithm to the semiparametric setup. Compared with the KBGD algorithm proposed by Khan et al. (2023) whose computational complexity is of order $O(n^2)$ in each update, the computational burden of our new estimator can be made close to $O(n)$, so can be easily applied when the sample size $n$ is large. Moreover, we show that if we further conduct averages across the estimators produced during iterations, the difference between the average estimator and KBGD estimator will be $n^{-1/2}$-trivial. Consequently, the average estimator is $n^{-1/2}$-consistent and asymptotically normally distributed. In other words, our new estimator substantially improves the computational speed, while at the same time maintains the estimation accuracy. We finally apply our new method to study how household age structure affects its risk preference and investing behavior. Using Chinese 2019 national survey data, we find that household with more elderly people is more likely to be risk averse and prefer risk-free assets

FairAdaBN: Mitigating unfairness with adaptive batch normalization and its application to dermatological disease classification

Deep learning is becoming increasingly ubiquitous in medical research and applications while involving sensitive information and even critical diagnosis decisions. Researchers observe a significant performance disparity among subgroups with different demographic attributes, which is called model unfairness, and put lots of effort into carefully designing elegant architectures to address unfairness, which poses heavy training burden, brings poor generalization, and reveals the trade-off between model performance and fairness. To tackle these issues, we propose FairAdaBN by making batch normalization adaptive to sensitive attribute. This simple but effective design can be adopted to several classification backbones that are originally unaware of fairness. Additionally, we derive a novel loss function that restrains statistical parity between subgroups on mini-batches, encouraging the model to converge with considerable fairness. In order to evaluate the trade-off between model performance and fairness, we propose a new metric, named Fairness-Accuracy Trade-off Efficiency (FATE), to compute normalized fairness improvement over accuracy drop. Experiments on two dermatological datasets show that our proposed method outperforms other methods on fairness criteria and FATE.Comment: Accepted by MICCAI 202

UOD: Universal One-shot Detection of Anatomical Landmarks

One-shot medical landmark detection gains much attention and achieves great success for its label-efficient training process. However, existing one-shot learning methods are highly specialized in a single domain and suffer domain preference heavily in the situation of multi-domain unlabeled data. Moreover, one-shot learning is not robust that it faces performance drop when annotating a sub-optimal image. To tackle these issues, we resort to developing a domain-adaptive one-shot landmark detection framework for handling multi-domain medical images, named Universal One-shot Detection (UOD). UOD consists of two stages and two corresponding universal models which are designed as combinations of domain-specific modules and domain-shared modules. In the first stage, a domain-adaptive convolution model is self-supervised learned to generate pseudo landmark labels. In the second stage, we design a domain-adaptive transformer to eliminate domain preference and build the global context for multi-domain data. Even though only one annotated sample from each domain is available for training, the domain-shared modules help UOD aggregate all one-shot samples to detect more robust and accurate landmarks. We investigated both qualitatively and quantitatively the proposed UOD on three widely-used public X-ray datasets in different anatomical domains (i.e., head, hand, chest) and obtained state-of-the-art performances in each domain.Comment: Eealy accepted by MICCAI 2023. 11pages, 4 figures, 2 table

Unsupervised augmentation optimization for few-shot medical image segmentation

The augmentation parameters matter to few-shot semantic segmentation since they directly affect the training outcome by feeding the networks with varying perturbated samples. However, searching optimal augmentation parameters for few-shot segmentation models without annotations is a challenge that current methods fail to address. In this paper, we first propose a framework to determine the ``optimal'' parameters without human annotations by solving a distribution-matching problem between the intra-instance and intra-class similarity distribution, with the intra-instance similarity describing the similarity between the original sample of a particular anatomy and its augmented ones and the intra-class similarity representing the similarity between the selected sample and the others in the same class. Extensive experiments demonstrate the superiority of our optimized augmentation in boosting few-shot segmentation models. We greatly improve the top competing method by 1.27\% and 1.11\% on Abd-MRI and Abd-CT datasets, respectively, and even achieve a significant improvement for SSL-ALP on the left kidney by 3.39\% on the Abd-CT dataset

Long-tailed multi-label classification with noisy label of thoracic diseases from chest X-ray

Chest X-rays (CXR) often reveal rare diseases, demanding precise diagnosis. However, current computer-aided diagnosis (CAD) methods focus on common diseases, leading to inadequate detection of rare conditions due to the absence of comprehensive datasets. To overcome this, we present a novel benchmark for long-tailed multi-label classification in CXRs, encapsulating both common and rare thoracic diseases. Our approach includes developing the "LTML-MIMIC-CXR" dataset, an augmentation of MIMIC-CXR with 26 additional rare diseases. We propose a baseline method for this classification challenge, integrating adaptive negative regularization to address negative logits' over-suppression in tail classes, and a large loss reconsideration strategy for correcting noisy labels from automated annotations. Our evaluation on LTML-MIMIC-CXR demonstrates significant advancements in rare disease detection. This work establishes a foundation for robust CAD methods, achieving a balance in identifying a spectrum of thoracic diseases in CXRs. Access to our code and dataset is provided at:https://github.com/laihaoran/LTML-MIMIC-CXR