856 research outputs found
3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation
Model architectures have been dramatically increasing in size, improving
performance at the cost of resource requirements. In this paper we propose 3DQ,
a ternary quantization method, applied for the first time to 3D Fully
Convolutional Neural Networks (F-CNNs), enabling 16x model compression while
maintaining performance on par with full precision models. We extensively
evaluate 3DQ on two datasets for the challenging task of whole brain
segmentation. Additionally, we showcase our method's ability to generalize on
two common 3D architectures, namely 3D U-Net and V-Net. Outperforming a variety
of baselines, the proposed method is capable of compressing large 3D models to
a few MBytes, alleviating the storage needs in space critical applications.Comment: Accepted to MICCAI 201
Modality-Agnostic Learning for Medical Image Segmentation Using Multi-modality Self-distillation
Medical image segmentation of tumors and organs at risk is a time-consuming
yet critical process in the clinic that utilizes multi-modality imaging (e.g,
different acquisitions, data types, and sequences) to increase segmentation
precision. In this paper, we propose a novel framework, Modality-Agnostic
learning through Multi-modality Self-dist-illation (MAG-MS), to investigate the
impact of input modalities on medical image segmentation. MAG-MS distills
knowledge from the fusion of multiple modalities and applies it to enhance
representation learning for individual modalities. Thus, it provides a
versatile and efficient approach to handle limited modalities during testing.
Our extensive experiments on benchmark datasets demonstrate the high efficiency
of MAG-MS and its superior segmentation performance than current
state-of-the-art methods. Furthermore, using MAG-MS, we provide valuable
insight and guidance on selecting input modalities for medical image
segmentation tasks
Continual Learning in Medical Image Analysis: A Comprehensive Review of Recent Advancements and Future Prospects
Medical imaging analysis has witnessed remarkable advancements even
surpassing human-level performance in recent years, driven by the rapid
development of advanced deep-learning algorithms. However, when the inference
dataset slightly differs from what the model has seen during one-time training,
the model performance is greatly compromised. The situation requires restarting
the training process using both the old and the new data which is
computationally costly, does not align with the human learning process, and
imposes storage constraints and privacy concerns. Alternatively, continual
learning has emerged as a crucial approach for developing unified and
sustainable deep models to deal with new classes, tasks, and the drifting
nature of data in non-stationary environments for various application areas.
Continual learning techniques enable models to adapt and accumulate knowledge
over time, which is essential for maintaining performance on evolving datasets
and novel tasks. This systematic review paper provides a comprehensive overview
of the state-of-the-art in continual learning techniques applied to medical
imaging analysis. We present an extensive survey of existing research, covering
topics including catastrophic forgetting, data drifts, stability, and
plasticity requirements. Further, an in-depth discussion of key components of a
continual learning framework such as continual learning scenarios, techniques,
evaluation schemes, and metrics is provided. Continual learning techniques
encompass various categories, including rehearsal, regularization,
architectural, and hybrid strategies. We assess the popularity and
applicability of continual learning categories in various medical sub-fields
like radiology and histopathology..
Towards Cross-modality Medical Image Segmentation with Online Mutual Knowledge Distillation
The success of deep convolutional neural networks is partially attributed to
the massive amount of annotated training data. However, in practice, medical
data annotations are usually expensive and time-consuming to be obtained.
Considering multi-modality data with the same anatomic structures are widely
available in clinic routine, in this paper, we aim to exploit the prior
knowledge (e.g., shape priors) learned from one modality (aka., assistant
modality) to improve the segmentation performance on another modality (aka.,
target modality) to make up annotation scarcity. To alleviate the learning
difficulties caused by modality-specific appearance discrepancy, we first
present an Image Alignment Module (IAM) to narrow the appearance gap between
assistant and target modality data.We then propose a novel Mutual Knowledge
Distillation (MKD) scheme to thoroughly exploit the modality-shared knowledge
to facilitate the target-modality segmentation. To be specific, we formulate
our framework as an integration of two individual segmentors. Each segmentor
not only explicitly extracts one modality knowledge from corresponding
annotations, but also implicitly explores another modality knowledge from its
counterpart in mutual-guided manner. The ensemble of two segmentors would
further integrate the knowledge from both modalities and generate reliable
segmentation results on target modality. Experimental results on the public
multi-class cardiac segmentation data, i.e., MMWHS 2017, show that our method
achieves large improvements on CT segmentation by utilizing additional MRI data
and outperforms other state-of-the-art multi-modality learning methods.Comment: Accepted by AAAI 202
BMAD: Benchmarks for Medical Anomaly Detection
Anomaly detection (AD) is a fundamental research problem in machine learning
and computer vision, with practical applications in industrial inspection,
video surveillance, and medical diagnosis. In medical imaging, AD is especially
vital for detecting and diagnosing anomalies that may indicate rare diseases or
conditions. However, there is a lack of a universal and fair benchmark for
evaluating AD methods on medical images, which hinders the development of more
generalized and robust AD methods in this specific domain. To bridge this gap,
we introduce a comprehensive evaluation benchmark for assessing anomaly
detection methods on medical images. This benchmark encompasses six reorganized
datasets from five medical domains (i.e. brain MRI, liver CT, retinal OCT,
chest X-ray, and digital histopathology) and three key evaluation metrics, and
includes a total of fourteen state-of-the-art AD algorithms. This standardized
and well-curated medical benchmark with the well-structured codebase enables
comprehensive comparisons among recently proposed anomaly detection methods. It
will facilitate the community to conduct a fair comparison and advance the
field of AD on medical imaging. More information on BMAD is available in our
GitHub repository: https://github.com/DorisBao/BMA
Dice Semimetric Losses: Optimizing the Dice Score with Soft Labels
The soft Dice loss (SDL) has taken a pivotal role in many automated
segmentation pipelines in the medical imaging community. Over the last years,
some reasons behind its superior functioning have been uncovered and further
optimizations have been explored. However, there is currently no implementation
that supports its direct use in settings with soft labels. Hence, a synergy
between the use of SDL and research leveraging the use of soft labels, also in
the context of model calibration, is still missing. In this work, we introduce
Dice semimetric losses (DMLs), which (i) are by design identical to SDL in a
standard setting with hard labels, but (ii) can be used in settings with soft
labels. Our experiments on the public QUBIQ, LiTS and KiTS benchmarks confirm
the potential synergy of DMLs with soft labels (e.g. averaging, label
smoothing, and knowledge distillation) over hard labels (e.g. majority voting
and random selection). As a result, we obtain superior Dice scores and model
calibration, which supports the wider adoption of DMLs in practice. Code is
available at
\href{https://github.com/zifuwanggg/JDTLosses}{https://github.com/zifuwanggg/JDTLosses}.Comment: Submitted to MICCAI2023. Code is available at
https://github.com/zifuwanggg/JDTLosse
Cross-Modality Deep Feature Learning for Brain Tumor Segmentation
Recent advances in machine learning and prevalence of digital medical images
have opened up an opportunity to address the challenging brain tumor
segmentation (BTS) task by using deep convolutional neural networks. However,
different from the RGB image data that are very widespread, the medical image
data used in brain tumor segmentation are relatively scarce in terms of the
data scale but contain the richer information in terms of the modality
property. To this end, this paper proposes a novel cross-modality deep feature
learning framework to segment brain tumors from the multi-modality MRI data.
The core idea is to mine rich patterns across the multi-modality data to make
up for the insufficient data scale. The proposed cross-modality deep feature
learning framework consists of two learning processes: the cross-modality
feature transition (CMFT) process and the cross-modality feature fusion (CMFF)
process, which aims at learning rich feature representations by transiting
knowledge across different modality data and fusing knowledge from different
modality data, respectively. Comprehensive experiments are conducted on the
BraTS benchmarks, which show that the proposed cross-modality deep feature
learning framework can effectively improve the brain tumor segmentation
performance when compared with the baseline methods and state-of-the-art
methods.Comment: published on Pattern Recognition 202
Continual Learning for Abdominal Multi-Organ and Tumor Segmentation
The ability to dynamically extend a model to new data and classes is critical
for multiple organ and tumor segmentation. However, due to privacy regulations,
accessing previous data and annotations can be problematic in the medical
domain. This poses a significant barrier to preserving the high segmentation
accuracy of the old classes when learning from new classes because of the
catastrophic forgetting problem. In this paper, we first empirically
demonstrate that simply using high-quality pseudo labels can fairly mitigate
this problem in the setting of organ segmentation. Furthermore, we put forward
an innovative architecture designed specifically for continuous organ and tumor
segmentation, which incurs minimal computational overhead. Our proposed design
involves replacing the conventional output layer with a suite of lightweight,
class-specific heads, thereby offering the flexibility to accommodate newly
emerging classes. These heads enable independent predictions for newly
introduced and previously learned classes, effectively minimizing the impact of
new classes on old ones during the course of continual learning. We further
propose incorporating Contrastive Language-Image Pretraining (CLIP) embeddings
into the organ-specific heads. These embeddings encapsulate the semantic
information of each class, informed by extensive image-text co-training. The
proposed method is evaluated on both in-house and public abdominal CT datasets
under organ and tumor segmentation tasks. Empirical results suggest that the
proposed design improves the segmentation performance of a baseline neural
network on newly-introduced and previously-learned classes along the learning
trajectory.Comment: MICCAI-202
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