Search Results for author:
Found 22 papers, 16 papers with code
On Transferability of Histological Tissue Labels in Computational Pathology
Deep learning tools in computational pathology, unlike natural vision tasks, face with limited histological tissue labels for classification.
End-to-End Supervised Multilabel Contrastive Learning
However, they fail to design an end-to-end training framework, leading to high computational complexity.
Computational Pathology: A Survey Review and The Way Forward
Computational Pathology CPath is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images.
Pseudo-Inverted Bottleneck Convolution for DARTS Search Space
Our proposed architecture is much less sensitive to evaluation layer count and outperforms a DARTS network with similar size significantly, at layer counts as small as 2.
Exploiting Explainable Metrics for Augmented SGD
In this paper, we address the following question: \textit{can we probe intermediate layers of a deep neural network to identify and quantify the learning quality of each layer?}
HistoKT: Cross Knowledge Transfer in Computational Pathology
In this paper, we take a data-centric approach to the transfer learning problem and examine the existence of generalizable knowledge between histopathological datasets.
Towards Robust and Automatic Hyper-Parameter Tunning
In this work, we introduce a new class of HPO method and explore how the low-rank factorization of the convolutional weights of intermediate layers of a convolutional neural network can be used to define an analytical response surface for optimizing hyper-parameters, using only training data.
NoFADE: Analyzing Diminishing Returns on CO2 Investment
Climate change continues to be a pressing issue that currently affects society at-large.
P4AI: Approaching AI Ethics through Principlism
The field of computer vision is rapidly evolving, particularly in the context of new methods of neural architecture design.
In Search of Probeable Generalization Measures
Understanding the generalization behaviour of deep neural networks is a topic of recent interest that has driven the production of many studies, notably the development and evaluation of generalization "explainability" measures that quantify model generalization ability.
CONetV2: Efficient Auto-Channel Size Optimization for CNNs
Neural Architecture Search (NAS) has been pivotal in finding optimal network configurations for Convolution Neural Networks (CNNs).
Probeable DARTS with Application to Computational Pathology
AI technology has made remarkable achievements in computational pathology (CPath), especially with the help of deep neural networks.
CONet: Channel Optimization for Convolutional Neural Networks
To solve this, we introduce an efficient dynamic scaling algorithm -- CONet -- that automatically optimizes channel sizes across network layers for a given CNN.
Reconsidering CO2 emissions from Computer Vision
To address these concerns, we propose adding "enforcement" as a pillar of ethical AI and provide some recommendations for how architecture designers and broader CV community can curb the climate crisis.
Response Modeling of Hyper-Parameters for Deep Convolution Neural Network
Hyper-parameter optimization (HPO) is critical in training high performing Deep Neural Networks (DNN).
FocusLiteNN: High Efficiency Focus Quality Assessment for Digital Pathology
Out-of-focus microscopy lens in digital pathology is a critical bottleneck in high-throughput Whole Slide Image (WSI) scanning platforms, for which pixel-level automated Focus Quality Assessment (FQA) methods are highly desirable to help significantly accelerate the clinical workflows.
AdaS: Adaptive Scheduling of Stochastic Gradients
The choice of step-size used in Stochastic Gradient Descent (SGD) optimization is empirically selected in most training procedures.
A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains
Our experiments indicate that histopathology and satellite images present a different set of problems for weakly-supervised semantic segmentation than natural scene images, such as ambiguous boundaries and class co-occurrence.
Atlas of Digital Pathology: A Generalized Hierarchical Histological Tissue Type-Annotated Database for Deep Learning
Quantitative results support the visually consistency of our data and we demonstrate a tissue type-based visual attention aid as a sample tool that could be developed from our database.
Focus Quality Assessment of High-Throughput Whole Slide Imaging in Digital Pathology
We also extend our method to generate a local slide-level focus quality heatmap, which can be used for automated slide quality control, and demonstrate the utility of our method for clinical scan quality control by comparison with subjective slide quality scores.
Convolutional Deblurring for Natural Imaging
In this paper, we propose a novel design of image deblurring in the form of one-shot convolution filtering that can directly convolve with naturally blurred images for restoration.
High-Accuracy Total Variation for Compressed Video Sensing
Numerous total variation (TV) regularizers, engaged in image restoration problem, encode the gradients by means of simple $[-1, 1]$ FIR filter.
