GAN-Based Super-Resolution And Segmentation Of Retinal Layers In Optical Coherence Tomography Scans

Optical Coherence Tomography (OCT) has been identified as a noninvasive and cost-effective imaging modality for identifying potential biomarkers for Alzheimer\u27s diagnosis and progress detection. Current hypotheses indicate that retinal layer thickness, which can be assessed via OCT scans, is an efficient biomarker for identifying Alzheimer\u27s disease. Due to factors such as speckle noise, a small target region, and unfavorable imaging conditions manual segmentation of retina layers is a challenging task. Therefore, as a reasonable first step, this study focuses on automatically segmenting retinal layers to separate them for subsequent investigations. Another important challenge commonly faced is the lack of clarity of the layer boundaries in retina OCT scans, which compels the research of super-resolving the images for improved clarity. Deep learning pipelines have stimulated substantial progress for the segmentation tasks. Generative adversarial networks (GANs) are a prominent field of deep learning which achieved astonishing performance in semantic segmentation. Conditional adversarial networks as a general-purpose solution to image-to-image translation problems not only learn the mapping from the input image to the output image but also learn a loss function to train this mapping. We propose a GAN-based segmentation model and evaluate incorporating popular networks, namely, U-Net and ResNet, in the GAN architecture with additional blocks of transposed convolution and sub-pixel convolution for the task of upscaling OCT images from low to high resolution by a factor of four. We also incorporate the Dice loss as an additional reconstruction loss term to improve the performance of this joint optimization task. Our best model configuration empirically achieved the Dice coefficient of 0.867 and mIOU of 0.765

RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment

Generative foundation models are susceptible to implicit biases that can arise from extensive unsupervised training data. Such biases can produce suboptimal samples, skewed outcomes, and unfairness, with potentially significant repercussions. Consequently, aligning these models with human ethics and preferences is an essential step toward ensuring their responsible and effective deployment in real-world applications. Prior research has primarily employed Reinforcement Learning from Human Feedback (RLHF) as a means of addressing this problem, wherein generative models are fine-tuned using RL algorithms guided by a human-feedback-informed reward model. However, the inefficiencies and instabilities associated with RL algorithms frequently present substantial obstacles to the successful alignment of generative models, necessitating the development of a more robust and streamlined approach. To this end, we introduce a new framework, Reward rAnked FineTuning (RAFT), designed to align generative models more effectively. Utilizing a reward model and a sufficient number of samples, our approach selects the high-quality samples, discarding those that exhibit undesired behavior, and subsequently assembles a streaming dataset. This dataset serves as the basis for aligning the generative model and can be employed under both offline and online settings. Notably, the sample generation process within RAFT is gradient-free, rendering it compatible with black-box generators. Through extensive experiments, we demonstrate that our proposed algorithm exhibits strong performance in the context of both large language models and diffusion models

PPCR: Learning Pyramid Pixel Context Recalibration Module for Medical Image Classification

Spatial attention mechanism has been widely incorporated into deep convolutional neural networks (CNNs) via long-range dependency capturing, significantly lifting the performance in computer vision, but it may perform poorly in medical imaging. Unfortunately, existing efforts are often unaware that long-range dependency capturing has limitations in highlighting subtle lesion regions, neglecting to exploit the potential of multi-scale pixel context information to improve the representational capability of CNNs. In this paper, we propose a practical yet lightweight architectural unit, Pyramid Pixel Context Recalibration (PPCR) module, which exploits multi-scale pixel context information to recalibrate pixel position in a pixel-independent manner adaptively. PPCR first designs a cross-channel pyramid pooling to aggregate multi-scale pixel context information, then eliminates the inconsistency among them by the well-designed pixel normalization, and finally estimates per pixel attention weight via a pixel context integration. PPCR can be flexibly plugged into modern CNNs with negligible overhead. Extensive experiments on five medical image datasets and CIFAR benchmarks empirically demonstrate the superiority and generalization of PPCR over state-of-the-art attention methods. The in-depth analyses explain the inherent behavior of PPCR in the decision-making process, improving the interpretability of CNNs.Comment: 10 page

A Comprehensive Overview of Large Language Models

Large Language Models (LLMs) have shown excellent generalization capabilities that have led to the development of numerous models. These models propose various new architectures, tweaking existing architectures with refined training strategies, increasing context length, using high-quality training data, and increasing training time to outperform baselines. Analyzing new developments is crucial for identifying changes that enhance training stability and improve generalization in LLMs. This survey paper comprehensively analyses the LLMs architectures and their categorization, training strategies, training datasets, and performance evaluations and discusses future research directions. Moreover, the paper also discusses the basic building blocks and concepts behind LLMs, followed by a complete overview of LLMs, including their important features and functions. Finally, the paper summarizes significant findings from LLM research and consolidates essential architectural and training strategies for developing advanced LLMs. Given the continuous advancements in LLMs, we intend to regularly update this paper by incorporating new sections and featuring the latest LLM models

An In-Depth Statistical Review of Retinal Image Processing Models from a Clinical Perspective

The burgeoning field of retinal image processing is critical in facilitating early diagnosis and treatment of retinal diseases, which are amongst the leading causes of vision impairment globally. Despite rapid advancements, existing machine learning models for retinal image processing are characterized by significant limitations, including disparities in pre-processing, segmentation, and classification methodologies, as well as inconsistencies in post-processing operations. These limitations hinder the realization of accurate, reliable, and clinically relevant outcomes. This paper provides an in-depth statistical review of extant machine learning models used in retinal image processing, meticulously comparing them based on their internal operating characteristics and performance levels. By adopting a robust analytical approach, our review delineates the strengths and weaknesses of current models, offering comprehensive insights that are instrumental in guiding future research and development in this domain. Furthermore, this review underscores the potential clinical impacts of these models, highlighting their pivotal role in enhancing diagnostic accuracy, prognostic assessments, and therapeutic interventions for retinal disorders. In conclusion, our work not only bridges the existing knowledge gap in the literature but also paves the way for the evolution of more sophisticated and clinically-aligned retinal image processing models, ultimately contributing to improved patient outcomes and advancements in ophthalmic care