A Knowledge Distillation Framework For Enhancing Ear-EEG Based Sleep Staging With Scalp-EEG Data

Sleep plays a crucial role in the well-being of human lives. Traditional sleep studies using Polysomnography are associated with discomfort and often lower sleep quality caused by the acquisition setup. Previous works have focused on developing less obtrusive methods to conduct high-quality sleep studies, and ear-EEG is among popular alternatives. However, the performance of sleep staging based on ear-EEG is still inferior to scalp-EEG based sleep staging. In order to address the performance gap between scalp-EEG and ear-EEG based sleep staging, we propose a cross-modal knowledge distillation strategy, which is a domain adaptation approach. Our experiments and analysis validate the effectiveness of the proposed approach with existing architectures, where it enhances the accuracy of the ear-EEG based sleep staging by 3.46% and Cohen's kappa coefficient by a margin of 0.038.Comment: Code available at : https://github.com/Mithunjha/EarEEG_KnowledgeDistillatio

From Hours to Seconds: Towards 100x Faster Quantitative Phase Imaging via Differentiable Microscopy

With applications ranging from metabolomics to histopathology, quantitative phase microscopy (QPM) is a powerful label-free imaging modality. Despite significant advances in fast multiplexed imaging sensors and deep-learning-based inverse solvers, the throughput of QPM is currently limited by the speed of electronic hardware. Complementarily, to improve throughput further, here we propose to acquire images in a compressed form such that more information can be transferred beyond the existing electronic hardware bottleneck. To this end, we present a learnable optical compression-decompression framework that learns content-specific features. The proposed differentiable quantitative phase microscopy ($\partial \mu$) first uses learnable optical feature extractors as image compressors. The intensity representation produced by these networks is then captured by the imaging sensor. Finally, a reconstruction network running on electronic hardware decompresses the QPM images. In numerical experiments, the proposed system achieves compression of $\times$ 64 while maintaining the SSIM of $\sim 0.90$ and PSNR of $\sim 30$ dB on cells. The results demonstrated by our experiments open up a new pathway for achieving end-to-end optimized (i.e., optics and electronic) compact QPM systems that may provide unprecedented throughput improvements

Contrastive Deep Encoding Enables Uncertainty-aware Machine-learning-assisted Histopathology

Deep neural network models can learn clinically relevant features from millions of histopathology images. However generating high-quality annotations to train such models for each hospital, each cancer type, and each diagnostic task is prohibitively laborious. On the other hand, terabytes of training data -- while lacking reliable annotations -- are readily available in the public domain in some cases. In this work, we explore how these large datasets can be consciously utilized to pre-train deep networks to encode informative representations. We then fine-tune our pre-trained models on a fraction of annotated training data to perform specific downstream tasks. We show that our approach can reach the state-of-the-art (SOTA) for patch-level classification with only 1-10% randomly selected annotations compared to other SOTA approaches. Moreover, we propose an uncertainty-aware loss function, to quantify the model confidence during inference. Quantified uncertainty helps experts select the best instances to label for further training. Our uncertainty-aware labeling reaches the SOTA with significantly fewer annotations compared to random labeling. Last, we demonstrate how our pre-trained encoders can surpass current SOTA for whole-slide image classification with weak supervision. Our work lays the foundation for data and task-agnostic pre-trained deep networks with quantified uncertainty.Comment: 18 pages, 8 figure

Towards Interpretable Sleep Stage Classification Using Cross-Modal Transformers

Accurate sleep stage classification is significant for sleep health assessment. In recent years, several machine-learning based sleep staging algorithms have been developed, and in particular, deep-learning based algorithms have achieved performance on par with human annotation. Despite the improved performance, a limitation of most deep-learning based algorithms is their black-box behavior, which has limited their use in clinical settings. Here, we propose a cross-modal transformer, which is a transformer-based method for sleep stage classification. The proposed cross-modal transformer consists of a novel cross-modal transformer encoder architecture along with a multi-scale one-dimensional convolutional neural network for automatic representation learning. Our method outperforms the state-of-the-art methods and eliminates the black-box behavior of deep-learning models by utilizing the interpretability aspect of the attention modules. Furthermore, our method provides considerable reductions in the number of parameters and training time compared to the state-of-the-art methods. Our code is available at https://github.com/Jathurshan0330/Cross-Modal-Transformer.Comment: 11 pages, 7 figures, 6 table

Real-time light field denoising using a novel linear 4-D hyperfan filter

Four-dimensional (4-D) light fields (LFs) enable novel imaging technologies, which are traditionally based on two-dimensional images. In most of these applications, denoising of LFs is required as a preprocessing technique before any subsequent processing. We propose a real-time LF denoising method using a novel 4-D linear and shift-invariant hyperfan filter. The proposed method exploits sparsity of the spectrum of a LF and the 4-D hyperfan filter is implemented in the 4-D mixed-domain (i.e.,two-dimensional space and two-dimensional frequency) leading to significant reductions in computational and memory complexities. A software implementation of the proposed method provides better or comparable denoising performance for grayscale and color LFs with respect to the metrics peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM) compared to previously reported linear LF denoising methods, while reducing the processing time approximately by 66% and 31% for grayscale and color LFs, respectively. Furthermore, we propose a semi-systolic hardware architecture for the proposed denoising method, and implement on a field-programmable gate array (FPGA). The FPGA implementation implies a throughput of 25 LFs/s for LFs of size 11x 11x 625x 434 and provides approximately 13 dB improvement in PSNR and 0.7 improvement in SSIM for grayscale LFs verifying the suitability for real-time processing

Multidimensional spatio-temporal filters for depth-velocity filtering in light field videos

A novel multidimensional spatio-temporal signal processing algorithm is proposed for depth-velocity filtering in 5-D light field videos along with a tutorial review of light field video spectra and the general architecture of associated 5-D depth-velocity filters. A 5-D FIR filter having a hyperfan-shaped passband which can selectively enhance the spectra of moving objects in light field videos based on their depth and motion trajectory is employed. A 5-D hyperfan-shaped passband is highly desired to achieve enhanced spatio-temporal selectivity over a wide range of lower frequencies. Experimental results are provided by using a light field videos captured using a Lytro light-field camera and mean structural similarity index is used to verify the effectiveness of the proposed 5-D FIR hyperfan depth-velocity filter

A 4-D sparse FIR hyperfan filter for volumetric refocusing of light fields by hard thresholding

A low-complexity 4-D sparse FIR hyperfan filter is proposed for volumetric refocusing of light fields. By exploiting the partial separability of the spectral region of support of a light field, the proposed filter is designed as a cascade of two 4-D hyperfan filters. The sparsity of the filter coefficients is achieved by hard thresholding the nonsparse filter coefficients. The experimental results confirm that the proposed 4-D sparse FIR hyperfan filter provides 72% mean reduction of computational complexity compared to a 4-D nonsparse FIR hyperfan filter withoudeteriorating the fidelity of volumetric refocused light fields. In particular, the mean structure similarity (SSIM) index between the volumetric refocused light fields by the proposed sparse filter and the nonsparse filter is 0.989

Improving the attenuation of moving interfering objects in videos using shifted-velocity filtering

Abstract Three-dimensional space-time velocity filters may be used to enhance dynamic passband objects of interest in videos while attenuating moving interfering objects based on their velocities. In this paper, we show that the attenuation of interfering stopband objects may be significantly improved using recently proposed shifted-velocity filters. It is shown that an improvement of approximately 20 dB in signal-to-interference ratio may be achieved for stopband to passband velocity differences of only 1 pixels/frame. More importantly, this improvement is achieved without increasing the computational complexity

Multi depth-velocity filters for enhancing multiple moving objects in 5-D light field videos

A moving object in a five-dimensional (5-D) light field video (LFV) can be selectively enhanced using the depth and the velocity of the object. In this paper, a 5-D depth-velocity (DV) filter is proposed to enhance multiple moving objects at different depths and with different velocities in an LFV. The 5-D DV filter is designed as a cascade of an infinite-extent impulse response multi-depth filter and a finite-extent impulse response multi-velocity filter. Experimental results obtained with numerically generated and real LFVs indicate that more than 15 dB improvement in signal-to-interference ratio can be achieved with the proposed 5-D multi DV filter compared to previously proposed multi depth-only filters

Differentiable Microscopy Designs an All Optical Quantitative Phase Microscope

Ever since the first microscope by Zacharias Janssen in the late 16th century, scientists have been inventing new types of microscopes for various tasks. Inventing a novel architecture demands years, if not decades, worth of scientific experience and creativity. In this work, we introduce Differentiable Microscopy ($\partial\mu$), a deep learning-based design paradigm, to aid scientists design new interpretable microscope architectures. Differentiable microscopy first models a common physics-based optical system however with trainable optical elements at key locations on the optical path. Using pre-acquired data, we then train the model end-to-end for a task of interest. The learnt design proposal can then be simplified by interpreting the learnt optical elements. As a first demonstration, based on the optical 4-$f$ system, we present an all-optical quantitative phase microscope (QPM) design that requires no computational post-reconstruction. A follow-up literature survey suggested that the learnt architecture is similar to the generalized phase contrast method developed two decades ago. Our extensive experiments on multiple datasets that include biological samples show that our learnt all-optical QPM designs consistently outperform existing methods. We experimentally verify the functionality of the optical 4-$f$ system based QPM design using a spatial light modulator. Furthermore, we also demonstrate that similar results can be achieved by an uninterpretable learning based method, namely diffractive deep neural networks (D2NN). The proposed differentiable microscopy framework supplements the creative process of designing new optical systems and would perhaps lead to unconventional but better optical designs