16 research outputs found
Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis
Tissue characterization has long been an important component of Computer
Aided Diagnosis (CAD) systems for automatic lesion detection and further
clinical planning. Motivated by the superior performance of deep learning
methods on various computer vision problems, there has been increasing work
applying deep learning to medical image analysis. However, the development of a
robust and reliable deep learning model for computer-aided diagnosis is still
highly challenging due to the combination of the high heterogeneity in the
medical images and the relative lack of training samples. Specifically,
annotation and labeling of the medical images is much more expensive and
time-consuming than other applications and often involves manual labor from
multiple domain experts. In this work, we propose a multi-stage, self-paced
learning framework utilizing a convolutional neural network (CNN) to classify
Computed Tomography (CT) image patches. The key contribution of this approach
is that we augment the size of training samples by refining the unlabeled
instances with a self-paced learning CNN. By implementing the framework on high
performance computing servers including the NVIDIA DGX1 machine, we obtained
the experimental result, showing that the self-pace boosted network
consistently outperformed the original network even with very scarce manual
labels. The performance gain indicates that applications with limited training
samples such as medical image analysis can benefit from using the proposed
framework.Comment: accepted by 8th International Workshop on Machine Learning in Medical
Imaging (MLMI 2017
RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Models
Large language model (LLM) applications in cloud root cause analysis (RCA)
have been actively explored recently. However, current methods are still
reliant on manual workflow settings and do not unleash LLMs' decision-making
and environment interaction capabilities. We present RCAgent, a tool-augmented
LLM autonomous agent framework for practical and privacy-aware industrial RCA
usage. Running on an internally deployed model rather than GPT families,
RCAgent is capable of free-form data collection and comprehensive analysis with
tools. Our framework combines a variety of enhancements, including a unique
Self-Consistency for action trajectories, and a suite of methods for context
management, stabilization, and importing domain knowledge. Our experiments show
RCAgent's evident and consistent superiority over ReAct across all aspects of
RCA -- predicting root causes, solutions, evidence, and responsibilities -- and
tasks covered or uncovered by current rules, as validated by both automated
metrics and human evaluations. Furthermore, RCAgent has already been integrated
into the diagnosis and issue discovery workflow of the Real-time Compute
Platform for Apache Flink of Alibaba Cloud
MediViSTA-SAM: Zero-shot Medical Video Analysis with Spatio-temporal SAM Adaptation
In recent years, the Segmentation Anything Model (SAM) has attracted
considerable attention as a foundational model well-known for its robust
generalization capabilities across various downstream tasks. However, SAM does
not exhibit satisfactory performance in the realm of medical image analysis. In
this study, we introduce the first study on adapting SAM on video segmentation,
called MediViSTA-SAM, a novel approach designed for medical video segmentation.
Given video data, MediViSTA, spatio-temporal adapter captures long and short
range temporal attention with cross-frame attention mechanism effectively
constraining it to consider the immediately preceding video frame as a
reference, while also considering spatial information effectively.
Additionally, it incorporates multi-scale fusion by employing a U-shaped
encoder and a modified mask decoder to handle objects of varying sizes. To
evaluate our approach, extensive experiments were conducted using
state-of-the-art (SOTA) methods, assessing its generalization abilities on
multi-vendor in-house echocardiography datasets. The results highlight the
accuracy and effectiveness of our network in medical video segmentation
MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation
The Segment Anything Model (SAM), a foundation model for general image
segmentation, has demonstrated impressive zero-shot performance across numerous
natural image segmentation tasks. However, SAM's performance significantly
declines when applied to medical images, primarily due to the substantial
disparity between natural and medical image domains. To effectively adapt SAM
to medical images, it is important to incorporate critical third-dimensional
information, i.e., volumetric or temporal knowledge, during fine-tuning.
Simultaneously, we aim to harness SAM's pre-trained weights within its original
2D backbone to the fullest extent. In this paper, we introduce a
modality-agnostic SAM adaptation framework, named as MA-SAM, that is applicable
to various volumetric and video medical data. Our method roots in the
parameter-efficient fine-tuning strategy to update only a small portion of
weight increments while preserving the majority of SAM's pre-trained weights.
By injecting a series of 3D adapters into the transformer blocks of the image
encoder, our method enables the pre-trained 2D backbone to extract
third-dimensional information from input data. The effectiveness of our method
has been comprehensively evaluated on four medical image segmentation tasks, by
using 10 public datasets across CT, MRI, and surgical video data. Remarkably,
without using any prompt, our method consistently outperforms various
state-of-the-art 3D approaches, surpassing nnU-Net by 0.9%, 2.6%, and 9.9% in
Dice for CT multi-organ segmentation, MRI prostate segmentation, and surgical
scene segmentation respectively. Our model also demonstrates strong
generalization, and excels in challenging tumor segmentation when prompts are
used. Our code is available at: https://github.com/cchen-cc/MA-SAM
From detection of individual metastases to classification of lymph node status at the patient level: the CAMELYON17 challenge
Automated detection of cancer metastases in lymph nodes has the potential to improve the assessment of prognosis for patients. To enable fair comparison between the algorithms for this purpose, we set up the CAMELYON17 challenge in conjunction with the IEEE International Symposium on Biomedical Imaging 2017 Conference in Melbourne. Over 300 participants registered on the challenge website, of which 23 teams submitted a total of 37 algorithms before the initial deadline. Participants were provided with 899 whole-slide images (WSIs) for developing their algorithms. The developed algorithms were evaluated based on the test set encompassing 100 patients and 500 WSIs. The evaluation metric used was a quadratic weighted Cohen's kappa. We discuss the algorithmic details of the 10 best pre-conference and two post-conference submissions. All these participants used convolutional neural networks in combination with pre- and postprocessing steps. Algorithms differed mostly in neural network architecture, training strategy, and pre- and postprocessing methodology. Overall, the kappa metric ranged from 0.89 to -0.13 across all submissions. The best results were obtained with pre-trained architectures such as ResNet. Confusion matrix analysis revealed that all participants struggled with reliably identifying isolated tumor cells, the smallest type of metastasis, with detection rates below 40%. Qualitative inspection of the results of the top participants showed categories of false positives, such as nerves or contamination, which could be targeted for further optimization. Last, we show that simple combinations of the top algorithms result in higher kappa metric values than any algorithm individually, with 0.93 for the best combination
Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer
Importance Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency.
Objective Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin–stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists’ diagnoses in a diagnostic setting.
Design, Setting, and Participants Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC).
Exposures Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation.
Main Outcomes and Measures The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor.
Results The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; P < .001). The top 5 algorithms had a mean AUC that was comparable with the pathologist interpreting the slides in the absence of time constraints (mean AUC, 0.960 [range, 0.923-0.994] for the top 5 algorithms vs 0.966 [95% CI, 0.927-0.998] for the pathologist WOTC).
Conclusions and Relevance In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting