42 research outputs found
Prospects for Theranostics in Neurosurgical Imaging: Empowering Confocal Laser Endomicroscopy Diagnostics via Deep Learning
Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence
imaging technology that has the potential to increase intraoperative precision,
extend resection, and tailor surgery for malignant invasive brain tumors
because of its subcellular dimension resolution. Despite its promising
diagnostic potential, interpreting the gray tone fluorescence images can be
difficult for untrained users. In this review, we provide a detailed
description of bioinformatical analysis methodology of CLE images that begins
to assist the neurosurgeon and pathologist to rapidly connect on-the-fly
intraoperative imaging, pathology, and surgical observation into a
conclusionary system within the concept of theranostics. We present an overview
and discuss deep learning models for automatic detection of the diagnostic CLE
images and discuss various training regimes and ensemble modeling effect on the
power of deep learning predictive models. Two major approaches reviewed in this
paper include the models that can automatically classify CLE images into
diagnostic/nondiagnostic, glioma/nonglioma, tumor/injury/normal categories and
models that can localize histological features on the CLE images using weakly
supervised methods. We also briefly review advances in the deep learning
approaches used for CLE image analysis in other organs. Significant advances in
speed and precision of automated diagnostic frame selection would augment the
diagnostic potential of CLE, improve operative workflow and integration into
brain tumor surgery. Such technology and bioinformatics analytics lend
themselves to improved precision, personalization, and theranostics in brain
tumor treatment.Comment: See the final version published in Frontiers in Oncology here:
https://www.frontiersin.org/articles/10.3389/fonc.2018.00240/ful
GenSelfDiff-HIS: Generative Self-Supervision Using Diffusion for Histopathological Image Segmentation
Histopathological image segmentation is a laborious and time-intensive task,
often requiring analysis from experienced pathologists for accurate
examinations. To reduce this burden, supervised machine-learning approaches
have been adopted using large-scale annotated datasets for histopathological
image analysis. However, in several scenarios, the availability of large-scale
annotated data is a bottleneck while training such models. Self-supervised
learning (SSL) is an alternative paradigm that provides some respite by
constructing models utilizing only the unannotated data which is often
abundant. The basic idea of SSL is to train a network to perform one or many
pseudo or pretext tasks on unannotated data and use it subsequently as the
basis for a variety of downstream tasks. It is seen that the success of SSL
depends critically on the considered pretext task. While there have been many
efforts in designing pretext tasks for classification problems, there haven't
been many attempts on SSL for histopathological segmentation. Motivated by
this, we propose an SSL approach for segmenting histopathological images via
generative diffusion models in this paper. Our method is based on the
observation that diffusion models effectively solve an image-to-image
translation task akin to a segmentation task. Hence, we propose generative
diffusion as the pretext task for histopathological image segmentation. We also
propose a multi-loss function-based fine-tuning for the downstream task. We
validate our method using several metrics on two publically available datasets
along with a newly proposed head and neck (HN) cancer dataset containing
hematoxylin and eosin (H\&E) stained images along with annotations. Codes will
be made public at
https://github.com/PurmaVishnuVardhanReddy/GenSelfDiff-HIS.git