4 research outputs found
CircleSnake: Instance Segmentation with Circle Representation
Circle representation has recently been introduced as a medical imaging
optimized representation for more effective instance object detection on
ball-shaped medical objects. With its superior performance on instance
detection, it is appealing to extend the circle representation to instance
medical object segmentation. In this work, we propose CircleSnake, a simple
end-to-end circle contour deformation-based segmentation method for ball-shaped
medical objects. Compared to the prevalent DeepSnake method, our contribution
is three-fold: (1) We replace the complicated bounding box to octagon contour
transformation with a computation-free and consistent bounding circle to circle
contour adaption for segmenting ball-shaped medical objects; (2) Circle
representation has fewer degrees of freedom (DoF=2) as compared with the
octagon representation (DoF=8), thus yielding a more robust segmentation
performance and better rotation consistency; (3) To the best of our knowledge,
the proposed CircleSnake method is the first end-to-end circle representation
deep segmentation pipeline method with consistent circle detection, circle
contour proposal, and circular convolution. The key innovation is to integrate
the circular graph convolution with circle detection into an end-to-end
instance segmentation framework, enabled by the proposed simple and consistent
circle contour representation. Glomeruli are used to evaluate the performance
of the benchmarks. From the results, CircleSnake increases the average
precision of glomerular detection from 0.559 to 0.614. The Dice score increased
from 0.804 to 0.849. The code has been released:
https://github.com/hrlblab/CircleSnakeComment: Machine Learning in Medical Imaging Workshop for 2022 MICCA
Digital Modeling on Large Kernel Metamaterial Neural Network
Deep neural networks (DNNs) utilized recently are physically deployed with
computational units (e.g., CPUs and GPUs). Such a design might lead to a heavy
computational burden, significant latency, and intensive power consumption,
which are critical limitations in applications such as the Internet of Things
(IoT), edge computing, and the usage of drones. Recent advances in optical
computational units (e.g., metamaterial) have shed light on energy-free and
light-speed neural networks. However, the digital design of the metamaterial
neural network (MNN) is fundamentally limited by its physical limitations, such
as precision, noise, and bandwidth during fabrication. Moreover, the unique
advantages of MNN's (e.g., light-speed computation) are not fully explored via
standard 3x3 convolution kernels. In this paper, we propose a novel large
kernel metamaterial neural network (LMNN) that maximizes the digital capacity
of the state-of-the-art (SOTA) MNN with model re-parametrization and network
compression, while also considering the optical limitation explicitly. The new
digital learning scheme can maximize the learning capacity of MNN while
modeling the physical restrictions of meta-optic. With the proposed LMNN, the
computation cost of the convolutional front-end can be offloaded into
fabricated optical hardware. The experimental results on two publicly available
datasets demonstrate that the optimized hybrid design improved classification
accuracy while reducing computational latency. The development of the proposed
LMNN is a promising step towards the ultimate goal of energy-free and
light-speed AI
Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-empowered Learning
Multi-class cell segmentation in high-resolution Giga-pixel whole slide
images (WSI) is critical for various clinical applications. Training such an AI
model typically requires labor-intensive pixel-wise manual annotation from
experienced domain experts (e.g., pathologists). Moreover, such annotation is
error-prone when differentiating fine-grained cell types (e.g., podocyte and
mesangial cells) via the naked human eye. In this study, we assess the
feasibility of democratizing pathological AI deployment by only using lay
annotators (annotators without medical domain knowledge). The contribution of
this paper is threefold: (1) We proposed a molecular-empowered learning scheme
for multi-class cell segmentation using partial labels from lay annotators; (2)
The proposed method integrated Giga-pixel level molecular-morphology
cross-modality registration, molecular-informed annotation, and
molecular-oriented segmentation model, so as to achieve significantly superior
performance via 3 lay annotators as compared with 2 experienced pathologists;
(3) A deep corrective learning (learning with imperfect label) method is
proposed to further improve the segmentation performance using partially
annotated noisy data. From the experimental results, our learning method
achieved F1 = 0.8496 using molecular-informed annotations from lay annotators,
which is better than conventional morphology-based annotations (F1 = 0.7051)
from experienced pathologists. Our method democratizes the development of a
pathological segmentation deep model to the lay annotator level, which
consequently scales up the learning process similar to a non-medical computer
vision task. The official implementation and cell annotations are publicly
available at https://github.com/hrlblab/MolecularEL
Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Images
Many anomaly detection approaches, especially deep learning methods, have
been recently developed to identify abnormal image morphology by only employing
normal images during training. Unfortunately, many prior anomaly detection
methods were optimized for a specific "known" abnormality (e.g., brain tumor,
bone fraction, cell types). Moreover, even though only the normal images were
used in the training process, the abnormal images were often employed during
the validation process (e.g., epoch selection, hyper-parameter tuning), which
might leak the supposed ``unknown" abnormality unintentionally. In this study,
we investigated these two essential aspects regarding universal anomaly
detection in medical images by (1) comparing various anomaly detection methods
across four medical datasets, (2) investigating the inevitable but often
neglected issues on how to unbiasedly select the optimal anomaly detection
model during the validation phase using only normal images, and (3) proposing a
simple decision-level ensemble method to leverage the advantage of different
kinds of anomaly detection without knowing the abnormality. The results of our
experiments indicate that none of the evaluated methods consistently achieved
the best performance across all datasets. Our proposed method enhanced the
robustness of performance in general (average AUC 0.956)