186 research outputs found
SenseCare: A Research Platform for Medical Image Informatics and Interactive 3D Visualization
Clinical research on smart healthcare has an increasing demand for
intelligent and clinic-oriented medical image computing algorithms and
platforms that support various applications. To this end, we have developed
SenseCare research platform for smart healthcare, which is designed to boost
translational research on intelligent diagnosis and treatment planning in
various clinical scenarios. To facilitate clinical research with Artificial
Intelligence (AI), SenseCare provides a range of AI toolkits for different
tasks, including image segmentation, registration, lesion and landmark
detection from various image modalities ranging from radiology to pathology. In
addition, SenseCare is clinic-oriented and supports a wide range of clinical
applications such as diagnosis and surgical planning for lung cancer, pelvic
tumor, coronary artery disease, etc. SenseCare provides several appealing
functions and features such as advanced 3D visualization, concurrent and
efficient web-based access, fast data synchronization and high data security,
multi-center deployment, support for collaborative research, etc. In this
paper, we will present an overview of SenseCare as an efficient platform
providing comprehensive toolkits and high extensibility for intelligent image
analysis and clinical research in different application scenarios.Comment: 11 pages, 10 figure
Signet ring cell detection from histological images using deep learning
Signet Ring Cell (SRC) Carcinoma is among the dangerous types of cancers, and has a major contribution towards the death ratio caused by cancerous diseases. Detection and diagnosis of SRC carcinoma at earlier stages is a challenging, laborious, and costly task. Automatic detection of SRCs in a patient's body through medical imaging by incorporating computing technologies is a hot topic of research. In the presented framework, we propose a novel approach that performs the identification and segmentation of SRCs in the histological images by using a deep learning (DL) technique named Mask Region-based Convolutional Neural Network (Mask-RCNN). In the first step, the input image is fed to Resnet-101 for feature extraction. The extracted feature maps are conveyed to Region Proposal Network (RPN) for the generation of the region of interest (RoI) proposals as well as they are directly conveyed to RoiAlign. Secondly, RoIAlign combines the feature maps with RoI proposals and generates segmentation masks by using a fully connected (FC) network and performs classification along with Bounding Box (bb) generation by using FC layers. The annotations are developed from ground truth (GT) images to perform experimentation on our developed dataset. Our introduced approach achieves accurate SRC detection with the precision and recall values of 0.901 and 0.897 respectively which can be utilized in clinical trials. We aim to release the employed database soon to assist the improvement in the SRC recognition research area
Tackling the Incomplete Annotation Issue in Universal Lesion Detection Task By Exploratory Training
Universal lesion detection has great value for clinical practice as it aims
to detect various types of lesions in multiple organs on medical images. Deep
learning methods have shown promising results, but demanding large volumes of
annotated data for training. However, annotating medical images is costly and
requires specialized knowledge. The diverse forms and contrasts of objects in
medical images make fully annotation even more challenging, resulting in
incomplete annotations. Directly training ULD detectors on such datasets can
yield suboptimal results. Pseudo-label-based methods examine the training data
and mine unlabelled objects for retraining, which have shown to be effective to
tackle this issue. Presently, top-performing methods rely on a dynamic
label-mining mechanism, operating at the mini-batch level. However, the model's
performance varies at different iterations, leading to inconsistencies in the
quality of the mined labels and limits their performance enhancement. Inspired
by the observation that deep models learn concepts with increasing complexity,
we introduce an innovative exploratory training to assess the reliability of
mined lesions over time. Specifically, we introduce a teacher-student detection
model as basis, where the teacher's predictions are combined with incomplete
annotations to train the student. Additionally, we design a prediction bank to
record high-confidence predictions. Each sample is trained several times,
allowing us to get a sequence of records for each sample. If a prediction
consistently appears in the record sequence, it is likely to be a true object,
otherwise it may just a noise. This serves as a crucial criterion for selecting
reliable mined lesions for retraining. Our experimental results substantiate
that the proposed framework surpasses state-of-the-art methods on two medical
image datasets, demonstrating its superior performance
Positive-unlabeled learning for binary and multi-class cell detection in histopathology images with incomplete annotations
Cell detection in histopathology images is of great interest to clinical
practice and research, and convolutional neural networks (CNNs) have achieved
remarkable cell detection results. Typically, to train CNN-based cell detection
models, every positive instance in the training images needs to be annotated,
and instances that are not labeled as positive are considered negative samples.
However, manual cell annotation is complicated due to the large number and
diversity of cells, and it can be difficult to ensure the annotation of every
positive instance. In many cases, only incomplete annotations are available,
where some of the positive instances are annotated and the others are not, and
the classification loss term for negative samples in typical network training
becomes incorrect. In this work, to address this problem of incomplete
annotations, we propose to reformulate the training of the detection network as
a positive-unlabeled learning problem. Since the instances in unannotated
regions can be either positive or negative, they have unknown labels. Using the
samples with unknown labels and the positively labeled samples, we first derive
an approximation of the classification loss term corresponding to negative
samples for binary cell detection, and based on this approximation we further
extend the proposed framework to multi-class cell detection. For evaluation,
experiments were performed on four publicly available datasets. The
experimental results show that our method improves the performance of cell
detection in histopathology images given incomplete annotations for network
training.Comment: Accepted for publication at the Journal of Machine Learning for
Biomedical Imaging (MELBA) https://melba-journal.org/2022:027. arXiv admin
note: text overlap with arXiv:2106.1591
Domain Generalization in Computational Pathology: Survey and Guidelines
Deep learning models have exhibited exceptional effectiveness in
Computational Pathology (CPath) by tackling intricate tasks across an array of
histology image analysis applications. Nevertheless, the presence of
out-of-distribution data (stemming from a multitude of sources such as
disparate imaging devices and diverse tissue preparation methods) can cause
\emph{domain shift} (DS). DS decreases the generalization of trained models to
unseen datasets with slightly different data distributions, prompting the need
for innovative \emph{domain generalization} (DG) solutions. Recognizing the
potential of DG methods to significantly influence diagnostic and prognostic
models in cancer studies and clinical practice, we present this survey along
with guidelines on achieving DG in CPath. We rigorously define various DS
types, systematically review and categorize existing DG approaches and
resources in CPath, and provide insights into their advantages, limitations,
and applicability. We also conduct thorough benchmarking experiments with 28
cutting-edge DG algorithms to address a complex DG problem. Our findings
suggest that careful experiment design and CPath-specific Stain Augmentation
technique can be very effective. However, there is no one-size-fits-all
solution for DG in CPath. Therefore, we establish clear guidelines for
detecting and managing DS depending on different scenarios. While most of the
concepts, guidelines, and recommendations are given for applications in CPath,
we believe that they are applicable to most medical image analysis tasks as
well.Comment: Extended Versio
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