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Adversarial Batch Representation Augmentation for Batch Correction in High-Content Cellular Screening
Authors:
Lei Tong,
Xujing Yao,
Adam Corrigan,
Long Chen,
Navin Rathna Kumar,
Kerry Hallbrook,
Jonathan Orme,
Yinhai Wang,
Huiyu Zhou
Abstract:
High-Content Screening routinely generates massive volumes of cell painting images for phenotypic profiling. However, technical variations across experimental executions inevitably induce biological batch (bio-batch) effects. These cause covariate shifts and degrade the generalization of deep learning models on unseen data. Existing batch correction methods typically rely on additional prior knowl…
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High-Content Screening routinely generates massive volumes of cell painting images for phenotypic profiling. However, technical variations across experimental executions inevitably induce biological batch (bio-batch) effects. These cause covariate shifts and degrade the generalization of deep learning models on unseen data. Existing batch correction methods typically rely on additional prior knowledge (e.g., treatment or cell culture information) or struggle to generalize to unseen bio-batches. In this work, we frame bio-batch mitigation as a Domain Generalization (DG) problem and propose Adversarial Batch Representation Augmentation (ABRA). ABRA explicitly models batch-wise statistical fluctuations by parameterizing feature statistics as structured uncertainties. Through a min-max optimization framework, it actively synthesizes worst-case bio-batch perturbations in the representation space, guided by a strict angular geometric margin to preserve fine-grained class discriminability. To prevent representation collapse during this adversarial exploration, we introduce a synergistic distribution alignment objective. Extensive evaluations on the large-scale RxRx1 and RxRx1-WILDS benchmarks demonstrate that ABRA establishes a new state-of-the-art for siRNA perturbation classification.
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Submitted 5 March, 2026;
originally announced March 2026.
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CLANet: A Comprehensive Framework for Cross-Batch Cell Line Identification Using Brightfield Images
Authors:
Lei Tong,
Adam Corrigan,
Navin Rathna Kumar,
Kerry Hallbrook,
Jonathan Orme,
Yinhai Wang,
Huiyu Zhou
Abstract:
Cell line authentication plays a crucial role in the biomedical field, ensuring researchers work with accurately identified cells. Supervised deep learning has made remarkable strides in cell line identification by studying cell morphological features through cell imaging. However, batch effects, a significant issue stemming from the different times at which data is generated, lead to substantial…
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Cell line authentication plays a crucial role in the biomedical field, ensuring researchers work with accurately identified cells. Supervised deep learning has made remarkable strides in cell line identification by studying cell morphological features through cell imaging. However, batch effects, a significant issue stemming from the different times at which data is generated, lead to substantial shifts in the underlying data distribution, thus complicating reliable differentiation between cell lines from distinct batch cultures. To address this challenge, we introduce CLANet, a pioneering framework for cross-batch cell line identification using brightfield images, specifically designed to tackle three distinct batch effects. We propose a cell cluster-level selection method to efficiently capture cell density variations, and a self-supervised learning strategy to manage image quality variations, thus producing reliable patch representations. Additionally, we adopt multiple instance learning(MIL) for effective aggregation of instance-level features for cell line identification. Our innovative time-series segment sampling module further enhances MIL's feature-learning capabilities, mitigating biases from varying incubation times across batches. We validate CLANet using data from 32 cell lines across 93 experimental batches from the AstraZeneca Global Cell Bank. Our results show that CLANet outperforms related approaches (e.g. domain adaptation, MIL), demonstrating its effectiveness in addressing batch effects in cell line identification.
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Submitted 28 June, 2023;
originally announced June 2023.
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An Analytical Approach for Project Managers in Effective Defect Management in Software Process
Authors:
T. R. Gopalakrishnan Nair,
V. Suma,
N. R. Shashi Kumar
Abstract:
Defect estimation and prediction are some of the main modulating factors for the success of software projects in any software industry. Maturity and competency of a project manager in efficient prediction and estimation of resource capabilities are one of the strategic driving forces towards the generation of high quality software. Currently, there are no estimation techniques developed through em…
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Defect estimation and prediction are some of the main modulating factors for the success of software projects in any software industry. Maturity and competency of a project manager in efficient prediction and estimation of resource capabilities are one of the strategic driving forces towards the generation of high quality software. Currently, there are no estimation techniques developed through empirical analysis to evaluate the decision capability of a project manager towards resource allocation for effective defect management. This paper brings out an empirical study carried out in a product based software organization. Our deep investigation on several projects throws light on the impact of decision capability of project manager towards accomplishment of an aforementioned objective. The paper enables project managers to gain further awareness towards the significance of predictive positioning in resource allocation in order to develop high quality defect-free software products. It also enhances the maturity level of the company and its persistence in the competitive atmosphere.
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Submitted 29 March, 2012;
originally announced March 2012.
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A novel super resolution reconstruction of low reoslution images progressively using dct and zonal filter based denoising
Authors:
Liyakathunisa,
C. N . Ravi Kumar
Abstract:
Due to the factors like processing power limitations and channel capabilities images are often down sampled and transmitted at low bit rates resulting in a low resolution compressed image. High resolution images can be reconstructed from several blurred, noisy and down sampled low resolution images using a computational process know as super resolution reconstruction. Super-resolution is the proce…
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Due to the factors like processing power limitations and channel capabilities images are often down sampled and transmitted at low bit rates resulting in a low resolution compressed image. High resolution images can be reconstructed from several blurred, noisy and down sampled low resolution images using a computational process know as super resolution reconstruction. Super-resolution is the process of combining multiple aliased low-quality images to produce a high resolution, high-quality image. The problem of recovering a high resolution image progressively from a sequence of low resolution compressed images is considered. In this paper we propose a novel DCT based progressive image display algorithm by stressing on the encoding and decoding process. At the encoder we consider a set of low resolution images which are corrupted by additive white Gaussian noise and motion blur. The low resolution images are compressed using 8 by 8 blocks DCT and noise is filtered using our proposed novel zonal filter. Multiframe fusion is performed in order to obtain a single noise free image. At the decoder the image is reconstructed progressively by transmitting the coarser image first followed by the detail image. And finally a super resolution image is reconstructed by applying our proposed novel adaptive interpolation technique. We have performed both objective and subjective analysis of the reconstructed image, and the resultant image has better super resolution factor, and a higher ISNR and PSNR. A comparative study done with Iterative Back Projection (IBP) and Projection on to Convex Sets (POCS),Papoulis Grechberg, FFT based Super resolution Reconstruction shows that our method has out performed the previous contributions.
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Submitted 28 February, 2011;
originally announced February 2011.
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GPRS Video Streaming Surveillance System GVSS
Authors:
T. P. Pushpavathi,
R Selvarani,
N. R. Shashi Kumar
Abstract:
Future security measures will create comfortable living environments that are embedded with a wide range of intelligent functionalities including home computing, entertainment, health care and security. These place stringent requirements on the home networking architecture which integrates various existing technologies for monitoring and control for future high security needs. This paper discuss…
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Future security measures will create comfortable living environments that are embedded with a wide range of intelligent functionalities including home computing, entertainment, health care and security. These place stringent requirements on the home networking architecture which integrates various existing technologies for monitoring and control for future high security needs. This paper discusses the design and implementation of a gvss gprs Video Streaming Surveillance System system, which integrates various existing technologies for providing security for smart home environments. This system provides security for office, home and other buildings where high security is required.This allows the mobile user to track the activities from a particular location. The system will send snapshots of the video and stores them in different formats. It is also possible to display the time with the image when it was captured in the gprs enabled mobiles. This system is implemented using J2me Technology
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Submitted 16 February, 2010;
originally announced February 2010.
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IT in Power Sector A KPCL Implementation
Authors:
T. P. Pushpavathi,
N. R. Shashi Kumar,
R. Selvarani
Abstract:
In this paper we investigate the extent of Information Technology penetration in Power sector, taking KPCL, Karnataka Power Corporation Ltd., a premier power generating, a state owned public sector organization as an example. Any organization to flourish, adoption of Information Technology is inevitable in the days of fast changing technological advancements. It is not merely the investment on I…
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In this paper we investigate the extent of Information Technology penetration in Power sector, taking KPCL, Karnataka Power Corporation Ltd., a premier power generating, a state owned public sector organization as an example. Any organization to flourish, adoption of Information Technology is inevitable in the days of fast changing technological advancements. It is not merely the investment on IT which helps but adoption of right IT solutions and the optimum use of the same does matter and becomes most critical. A strong infrastructure coupled with modern technical and management concepts has helped KPCL to meet the challenges of the rising energy demands of Karnataka.
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Submitted 13 February, 2010;
originally announced February 2010.