30 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
Computational Pathology: A Survey Review and The Way Forward
Computational Pathology CPath is an interdisciplinary science that augments
developments of computational approaches to analyze and model medical
histopathology images. The main objective for CPath is to develop
infrastructure and workflows of digital diagnostics as an assistive CAD system
for clinical pathology, facilitating transformational changes in the diagnosis
and treatment of cancer that are mainly address by CPath tools. With
evergrowing developments in deep learning and computer vision algorithms, and
the ease of the data flow from digital pathology, currently CPath is witnessing
a paradigm shift. Despite the sheer volume of engineering and scientific works
being introduced for cancer image analysis, there is still a considerable gap
of adopting and integrating these algorithms in clinical practice. This raises
a significant question regarding the direction and trends that are undertaken
in CPath. In this article we provide a comprehensive review of more than 800
papers to address the challenges faced in problem design all-the-way to the
application and implementation viewpoints. We have catalogued each paper into a
model-card by examining the key works and challenges faced to layout the
current landscape in CPath. We hope this helps the community to locate relevant
works and facilitate understanding of the field's future directions. In a
nutshell, we oversee the CPath developments in cycle of stages which are
required to be cohesively linked together to address the challenges associated
with such multidisciplinary science. We overview this cycle from different
perspectives of data-centric, model-centric, and application-centric problems.
We finally sketch remaining challenges and provide directions for future
technical developments and clinical integration of CPath
(https://github.com/AtlasAnalyticsLab/CPath_Survey).Comment: Accepted in Elsevier Journal of Pathology Informatics (JPI) 202