26 research outputs found
Scalarization for Multi-Task and Multi-Domain Learning at Scale
Training a single model on multiple input domains and/or output tasks allows
for compressing information from multiple sources into a unified backbone hence
improves model efficiency. It also enables potential positive knowledge
transfer across tasks/domains, leading to improved accuracy and data-efficient
training. However, optimizing such networks is a challenge, in particular due
to discrepancies between the different tasks or domains: Despite several
hypotheses and solutions proposed over the years, recent work has shown that
uniform scalarization training, i.e., simply minimizing the average of the task
losses, yields on-par performance with more costly SotA optimization methods.
This raises the issue of how well we understand the training dynamics of
multi-task and multi-domain networks. In this work, we first devise a
large-scale unified analysis of multi-domain and multi-task learning to better
understand the dynamics of scalarization across varied task/domain combinations
and model sizes. Following these insights, we then propose to leverage
population-based training to efficiently search for the optimal scalarization
weights when dealing with a large number of tasks or domains.Comment: NeurIPS 2023; https://openreview.net/forum?id=TSuq3debn
Novel chromatin texture features for the classification of Pap smears
This paper presents a set of novel structural texture features for quantifying nuclear chromatin patterns in cells on a conventional Pap smear. The features are derived from an initial segmentation of the chromatin into bloblike texture primitives. The results of a comprehensive feature selection experiment, including the set of proposed structural texture features and a range of different cytology features drawn from the literature, show that two of the four top ranking features are structural texture features. They also show that a combination of structural and conventional features yields a classification performance of 0.954±0.019 (AUC±SE) for the discrimination of normal (NILM) and abnormal (LSIL and HSIL) slides. The results of a second classification experiment, using only normal-appearing cells from both normal and abnormal slides, demonstrates that a single structural texture feature measuring chromatin margination yields a classification performance of 0.815±0.019. Overall the results demonstrate the efficacy of the proposed structural approach and that it is possible to detect malignancy associated changes (MACs) in Papanicoloau stain
Conditional Channel Gated Networks for Task-Aware Continual Learning
Convolutional Neural Networks experience catastrophic forgetting when
optimized on a sequence of learning problems: as they meet the objective of the
current training examples, their performance on previous tasks drops
drastically. In this work, we introduce a novel framework to tackle this
problem with conditional computation. We equip each convolutional layer with
task-specific gating modules, selecting which filters to apply on the given
input. This way, we achieve two appealing properties. Firstly, the execution
patterns of the gates allow to identify and protect important filters, ensuring
no loss in the performance of the model for previously learned tasks. Secondly,
by using a sparsity objective, we can promote the selection of a limited set of
kernels, allowing to retain sufficient model capacity to digest new
tasks.Existing solutions require, at test time, awareness of the task to which
each example belongs to. This knowledge, however, may not be available in many
practical scenarios. Therefore, we additionally introduce a task classifier
that predicts the task label of each example, to deal with settings in which a
task oracle is not available. We validate our proposal on four continual
learning datasets. Results show that our model consistently outperforms
existing methods both in the presence and the absence of a task oracle.
Notably, on Split SVHN and Imagenet-50 datasets, our model yields up to 23.98%
and 17.42% improvement in accuracy w.r.t. competing methods.Comment: CVPR 2020 (oral
Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images
Automated classification of histopathological whole-slide images (WSI) of
breast tissue requires analysis at very high resolutions with a large
contextual area. In this paper, we present context-aware stacked convolutional
neural networks (CNN) for classification of breast WSIs into normal/benign,
ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC). We first
train a CNN using high pixel resolution patches to capture cellular level
information. The feature responses generated by this model are then fed as
input to a second CNN, stacked on top of the first. Training of this stacked
architecture with large input patches enables learning of fine-grained
(cellular) details and global interdependence of tissue structures. Our system
is trained and evaluated on a dataset containing 221 WSIs of H&E stained breast
tissue specimens. The system achieves an AUC of 0.962 for the binary
classification of non-malignant and malignant slides and obtains a three class
accuracy of 81.3% for classification of WSIs into normal/benign, DCIS, and IDC,
demonstrating its potentials for routine diagnostics
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Predicting breast tumor proliferation from whole-slide images : the TUPAC16 challenge
Tumor proliferation is an important biomarker indicative of the prognosis of breast cancer patients. Assessment of tumor proliferation in a clinical setting is a highly subjective and labor-intensive task. Previous efforts to automate tumor proliferation assessment by image analysis only focused on mitosis detection in predefined tumor regions. However, in a real-world scenario, automatic mitosis detection should be performed in whole-slide images (WSIs) and an automatic method should be able to produce a tumor proliferation score given a WSI as input. To address this, we organized the TUmor Proliferation Assessment Challenge 2016 (TUPAC16) on prediction of tumor proliferation scores from WSIs.
The challenge dataset consisted of 500 training and 321 testing breast cancer histopathology WSIs. In order to ensure fair and independent evaluation, only the ground truth for the training dataset was provided to the challenge participants. The first task of the challenge was to predict mitotic scores, i.e., to reproduce the manual method of assessing tumor proliferation by a pathologist. The second task was to predict the gene expression based PAM50 proliferation scores from the WSI.
The best performing automatic method for the first task achieved a quadratic-weighted Cohen's kappa score of κ = 0.567, 95% CI [0.464, 0.671] between the predicted scores and the ground truth. For the second task, the predictions of the top method had a Spearman's correlation coefficient of r = 0.617, 95% CI [0.581 0.651] with the ground truth.
This was the first comparison study that investigated tumor proliferation assessment from WSIs. The achieved results are promising given the difficulty of the tasks and weakly-labeled nature of the ground truth. However, further research is needed to improve the practical utility of image analysis methods for this task