5 research outputs found
Relational Learning between Multiple Pulmonary Nodules via Deep Set Attention Transformers
Diagnosis and treatment of multiple pulmonary nodules are clinically
important but challenging. Prior studies on nodule characterization use
solitary-nodule approaches on multiple nodular patients, which ignores the
relations between nodules. In this study, we propose a multiple instance
learning (MIL) approach and empirically prove the benefit to learn the
relations between multiple nodules. By treating the multiple nodules from a
same patient as a whole, critical relational information between
solitary-nodule voxels is extracted. To our knowledge, it is the first study to
learn the relations between multiple pulmonary nodules. Inspired by recent
advances in natural language processing (NLP) domain, we introduce a
self-attention transformer equipped with 3D CNN, named {NoduleSAT}, to replace
typical pooling-based aggregation in multiple instance learning. Extensive
experiments on lung nodule false positive reduction on LUNA16 database, and
malignancy classification on LIDC-IDRI database, validate the effectiveness of
the proposed method.Comment: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI
2020
Hierarchical Classification of Pulmonary Lesions: A Large-Scale Radio-Pathomics Study
Diagnosis of pulmonary lesions from computed tomography (CT) is important but
challenging for clinical decision making in lung cancer related diseases. Deep
learning has achieved great success in computer aided diagnosis (CADx) area for
lung cancer, whereas it suffers from label ambiguity due to the difficulty in
the radiological diagnosis. Considering that invasive pathological analysis
serves as the clinical golden standard of lung cancer diagnosis, in this study,
we solve the label ambiguity issue via a large-scale radio-pathomics dataset
containing 5,134 radiological CT images with pathologically confirmed labels,
including cancers (e.g., invasive/non-invasive adenocarcinoma, squamous
carcinoma) and non-cancer diseases (e.g., tuberculosis, hamartoma). This
retrospective dataset, named Pulmonary-RadPath, enables development and
validation of accurate deep learning systems to predict invasive pathological
labels with a non-invasive procedure, i.e., radiological CT scans. A
three-level hierarchical classification system for pulmonary lesions is
developed, which covers most diseases in cancer-related diagnosis. We explore
several techniques for hierarchical classification on this dataset, and propose
a Leaky Dense Hierarchy approach with proven effectiveness in experiments. Our
study significantly outperforms prior arts in terms of data scales (6x larger),
disease comprehensiveness and hierarchies. The promising results suggest the
potentials to facilitate precision medicine.Comment: MICCAI 2020 (Early Accepted
MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response
Predicting clinical outcome is remarkably important but challenging. Research
efforts have been paid on seeking significant biomarkers associated with the
therapy response or/and patient survival. However, these biomarkers are
generally costly and invasive, and possibly dissatifactory for novel therapy.
On the other hand, multi-modal, heterogeneous, unaligned temporal data is
continuously generated in clinical practice. This paper aims at a unified deep
learning approach to predict patient prognosis and therapy response, with
easily accessible data, e.g., radiographics, laboratory and clinical
information. Prior arts focus on modeling single data modality, or ignore the
temporal changes. Importantly, the clinical time series is asynchronous in
practice, i.e., recorded with irregular intervals. In this study, we formalize
the prognosis modeling as a multi-modal asynchronous time series classification
task, and propose a MIA-Prognosis framework with Measurement, Intervention and
Assessment (MIA) information to predict therapy response, where a Simple
Temporal Attention (SimTA) module is developed to process the asynchronous time
series. Experiments on synthetic dataset validate the superiory of SimTA over
standard RNN-based approaches. Furthermore, we experiment the proposed method
on an in-house, retrospective dataset of real-world non-small cell lung cancer
patients under anti-PD-1 immunotherapy. The proposed method achieves promising
performance on predicting the immunotherapy response. Notably, our predictive
model could further stratify low-risk and high-risk patients in terms of
long-term survival.Comment: MICCAI 2020 (Early Accepted; Student Travel Award
Improving diagnosis and prognosis of lung cancer using vision transformers: A scoping review
Vision transformer-based methods are advancing the field of medical
artificial intelligence and cancer imaging, including lung cancer applications.
Recently, many researchers have developed vision transformer-based AI methods
for lung cancer diagnosis and prognosis. This scoping review aims to identify
the recent developments on vision transformer-based AI methods for lung cancer
imaging applications. It provides key insights into how vision transformers
complemented the performance of AI and deep learning methods for lung cancer.
Furthermore, the review also identifies the datasets that contributed to
advancing the field. Of the 314 retrieved studies, this review included 34
studies published from 2020 to 2022. The most commonly addressed task in these
studies was the classification of lung cancer types, such as lung squamous cell
carcinoma versus lung adenocarcinoma, and identifying benign versus malignant
pulmonary nodules. Other applications included survival prediction of lung
cancer patients and segmentation of lungs. The studies lacked clear strategies
for clinical transformation. SWIN transformer was a popular choice of the
researchers; however, many other architectures were also reported where vision
transformer was combined with convolutional neural networks or UNet model. It
can be concluded that vision transformer-based models are increasingly in
popularity for developing AI methods for lung cancer applications. However,
their computational complexity and clinical relevance are important factors to
be considered for future research work. This review provides valuable insights
for researchers in the field of AI and healthcare to advance the
state-of-the-art in lung cancer diagnosis and prognosis. We provide an
interactive dashboard on lung-cancer.onrender.com/.Comment: submitted to BMC Medical Imaging journa