arXiv subjects

Dan Hu

Publications and source records attributed to Dan Hu.

At least 19 recordsLinked to original sources

arXiv2026

Vehicle routing problem using deep reinforcement learning - A case study about truck planning in the industry

As an important component of the supply chain industry, transportation has experienced rapid development in the past decade with the assistance of digital platforms and intelligent algorithms. Within the field of transportation research, Vehicle Routing Problem (VRP) has remained a persistent and enduring challenge. In the realm of management science, experts, and scholars from both the industrial and academic sectors have continuously explored optimization models and algorithms to effectively address routing problems, from the classical Traveling Salesman Problem to the more general Vehicle Routing Problem. These models and algorithms are applied in real-world industrial scenarios to achieve cost optimization and reduce carbon footprints. However, due to the complexity of real-world problems, numerous specific constraints are often added, and challenges such as information opacity, uncertainty, and irrational human behavior may arise. Therefore, deploying and optimizing mathematical models for VRP in practical scenarios while maintaining optimal results poses numerous challenges. This paper discusses and provides solutions for three different logistic use cases involving external truck network design. Through these industrial case study, the paper introduces how deep reinforcement learning-based vehicle routing optimization has been implemented. As a result, it can be observed that the routes optimized by reinforcement learning agent have over 10% total cost compared to baseline results. Furthermore, the paper proposes that in future research, DRL algorithms for vehicle routing problems could be generalized into more variations of VRP.

cs.AI↗

arXiv2026

Metal sign of a large-scale AGN feedback in cool-core cluster MACS J1931.8-2634

The spatial distribution of metals in the intracluster medium (ICM) is a sensitive tracer of the chemical and dynamical history of galaxy clusters. While most cool-core (CC) clusters exhibit a centrally peaked Fe abundance profile, several outliers show an anomalous central Fe drop, potentially associated with the AGN activities. We revisit the reported large-scale (sim 100 kpc) central Fe drop in the massive CC cluster MACS J1931.8-2634 using new XMM-Newton observations. We aim to verify this feature and search for imprints of AGN feedback on the ICM metallicity distribution. We analyzed sim 170 ks of new XMM-Newton observations and re-analyzed sim 100 ks archived Chandra observations. We derived radial and two-dimensional (2D) Fe abundance maps from CCD spectra. High-resolution RGS spectra were used to constrain the Ne/Fe abundance ratio to test the dust depletion scenario. Spectral fitting was performed in SPEX using an updated atomic database and both single- and multi-temperature collisional ionization equilibrium models. The previously reported central Fe drop is not confirmed in the radial profile from XMM-Newton. However, the 2D Fe distribution is clearly asymmetric: Fe-rich regions are elongated along the axis of the AGN cavities, extending beyond their immediate scale. The Ne/Fe ratio in the core is consistent with solar (${\rm Ne/Fe} = 1.03^{+0.25}_{-0.23}$), arguing against the dust depletion scenario.

astro-ph.HE↗

arXiv2026

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis

Pathological diagnosis is inherently multi-scale, requiring the integration of global tissue architecture at low magnification with cellular morphology at higher magnification. However, existing pathology benchmarks and vision-language models (VLMs) are still largely developed under single-scale settings, limiting their ability to learn clinically meaningful multi-magnification reasoning. Moreover, naively constructed visual question answering (VQA) tasks may be susceptible to text-only or superficial visual shortcuts, leading to unreliable assessments of visual understanding. To address these limitations, we introduce a benchmark and training framework for shortcut-resistant cross-scale pathology reasoning. We design an Adversarial Text-only Screening strategy for semantic reasoning questions and a Structure-controlled Distractor Sampling strategy for visual grounding questions, encouraging models to rely on cross-scale visual evidence. Based on this pipeline, we construct PathScale-VQA, a high-quality cross-scale pathology VQA benchmark with 10,373 multiple-choice questions grounded in 1,368 diagnostic paths across multiple magnification levels. Building on the semantic reasoning set, PathScale-R1 is optimized through Difficulty-driven Reasoning Distillation supervised fine-tuning followed by reinforcement learning with a Scale-aware Reasoning Structure reward, which encourages the use of evidence across magnifications. Extensive experiments demonstrate state-of-the-art performance of PathScale-R1 on cross-scale reasoning tasks and effective transfer to conventional single-scale pathology VQA. Our code is available at https://github.com/iMVR-PL/PathScale-R1.

cs.CV↗

arXiv2026

Enhancing Pathological VLMs with Cross-scale Reasoning

Pathological images are inherently multi-scale, requiring pathologists to integrate evidence from global tissue architecture at low magnification to cellular morphology at higher magnification for accurate diagnosis. While existing pathological datasets for vision-language models (VLMs) include various scales, they often lack explicit cross-scale reasoning objectives. This limitation prevents VLMs from capturing essential cross-scale representations and learning evidence-based reasoning. To bridge this gap, we introduce the first cross-scale training and evaluation paradigm that formulates pathology interpretation as multi-magnification reasoning. However, creating such a task reveals a critical challenge: multi-image visual question answering (VQA) is prone to text-only shortcuts, which allow models to guess answers using magnification-dependent artifacts rather than visual evidence. To address this, we propose a leakage-aware curation pipeline that combines adversarial text-only screening with constraint-guided question design. Using this pipeline, we construct Scale-VQA, a high-quality benchmark with 4,685 multiple-choice questions grounded in 2,537 pathology images across multiple magnification levels. Finally, we present ScaleReasoner-R1, a model trained via reinforcement learning to optimize performance on cross-scale VQA tasks. ScaleReasoner-R1 achieves state-of-the-art performance on our cross-scale reasoning benchmark and generalizes to SOTA performance on established single-scale benchmarks. Findings suggest that even the limited cross-scale supervision can significantly improve pathological understanding. Code is available at https://github.com/iMVR-PL/ScaleReasoner-R1.

cs.CV↗

arXiv2026

S-GRPO: Unified Post-Training for Large Vision-Language Models

Current post-training methodologies for adapting Large Vision-Language Models (LVLMs) generally fall into two paradigms: Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL). Despite their prevalence, both approaches suffer from inefficiencies when applied in isolation. SFT forces the model's generation along a single expert trajectory, often inducing catastrophic forgetting of general multimodal capabilities due to distributional shifts. Conversely, RL explores multiple generated trajectories but frequently encounters optimization collapse - a cold-start problem where an unaligned model fails to spontaneously sample any domain-valid trajectories in sparse-reward visual tasks. In this paper, we propose Supervised Group Relative Policy Optimization (S-GRPO), a unified post-training framework that integrates the guidance of imitation learning into the multi-trajectory exploration of preference optimization. Tailored for direct-generation visual tasks, S-GRPO introduces Conditional Ground-Truth Trajectory Injection (CGI). When a binary verifier detects a complete exploratory failure within a sampled group of trajectories, CGI injects the verified ground-truth trajectory into the candidate pool. By assigning a deterministic maximal reward to this injected anchor, S-GRPO enforces a positive signal within the group-relative advantage estimation. This mechanism reformulates the supervised learning objective as a high-advantage component of the policy gradient, compelling the model to dynamically balance between exploiting the expert trajectory and exploring novel visual concepts. Theoretical analysis and empirical results demonstrate that S-GRPO gracefully bridges the gap between SFT and RL, drastically accelerates convergence, and achieves superior domain adaptation while preserving the base model's general-purpose capabilities.

cs.LG↗

arXiv2026

Reinforced Curriculum Pre-Alignment for Domain-Adaptive VLMs

Vision-Language Models (VLMs) demonstrate remarkable general-purpose capabilities but often fall short in specialized domains such as medical imaging or geometric problem-solving. Supervised Fine-Tuning (SFT) can enhance performance within a target domain, but it typically causes catastrophic forgetting, limiting its generalization. The central challenge, therefore, is to adapt VLMs to new domains while preserving their general-purpose capabilities. Continual pretraining is effective for expanding knowledge in Large Language Models (LLMs), but it is less feasible for VLMs due to prohibitive computational costs and the unavailability of pretraining data for most open-source models. This necessitates efficient post-training adaptation methods. Reinforcement learning (RL)-based approaches such as Group Relative Policy Optimization (GRPO) have shown promise in preserving general abilities, yet they often fail in domain adaptation scenarios where the model initially lacks sufficient domain knowledge, leading to optimization collapse. To bridge this gap, we propose Reinforced Curriculum Pre-Alignment (RCPA), a novel post-training paradigm that introduces a curriculum-aware progressive modulation mechanism. In the early phase, RCPA applies partial output constraints to safely expose the model to new domain concepts. As the model's domain familiarity increases, training gradually transitions to full generation optimization, refining responses and aligning them with domain-specific preferences. This staged adaptation balances domain knowledge acquisition with the preservation of general multimodal capabilities. Extensive experiments across specialized domains and general benchmarks validate the effectiveness of RCPA, establishing a practical pathway toward building high-performing and domain-adaptive VLMs.

cs.CL↗

arXiv2025

A Chandra view of SPT-CL J0217-5014: a massive galaxy cluster at a cosmic intersection at z=0.53

Galaxy clusters trace the densest regions of the cosmic web and are crucial laboratories for studying the thermodynamic and chemical evolution of the intracluster medium (ICM). We present a Chandra study of the massive galaxy cluster SPT-CL J0217-5014 ($z \sim 0.53$; $M_{\rm 500} \sim 3 \times 10^{14}~\rm M_{\odot}$), previously reported as a Swift serendipitous clusters with the highest Fe abundance ($\sim 1.3\pm 0.4$ $\rm Z_{\odot}$ within $\sim 1'.7$) and a potentially disturbed morphology. The X-ray morphology reveals a disturbed ICM with a surface brightness edge at $\sim 0'.26$ ($\sim 100$ kpc) to the west and a tail-like feature extending towards the east. The best-fit metal abundance within 1'.5 ($\sim 0.7\rm R_{500}$) is $0.61_{-0.23}^{+0.26}~\rm Z_{\odot}$. The derived central electron number density, entropy, and cooling time classify this system as a non-cool-core cluster, suggesting that merger activity has likely disrupted the possible pre-existing cool core. At larger radii ($\sim 1' - 2'$), we detect excess X-ray emission to the south, spatially aligned with a filamentary distribution of red galaxies, indicating ongoing accretion along an intracluster filament. Based on the DESI DR9 cross-matched optical clusters and photometric redshifts, we identify three nearby, lower-mass clusters that likely trace the large-scale structures, suggesting that SPT-CL~J0217-5014 is the primary node of a dynamically active environment where past mergers and anisotropic accretion along cosmic filaments have shaped the present-day ICM.

astro-ph.GA↗

arXiv2025

Suzaku Observation of Merging Clusters Abell 222 and Abell 223

Previous X-ray and optical studies of the galaxy cluster pair Abell 222/223 suggested the possible presence of a filamentary structure connecting the two clusters, a result that appears to be supported by subsequent weak-lensing analyses. This filament has been reported to host a primordial warm-hot intergalactic medium (WHIM), which existed prior to being heated by the interactions of the clusters. In this study, we made an attempt to examine the reported emission feature with data from an archival Suzaku observation, taking advantage of its low detector background. Because the emission is expected to be very weak, we first carefully examined all potential sources of "contamination", and then modelled the residual emission. Due to large uncertainties, unfortunately, our results can neither confirm the presence of the reported emission feature nor rule it out. We discuss the sources of uncertainties.

astro-ph.CO↗

arXiv2025

MWA and VLA Observations of Diffuse Radio Lobes in M 87

This study investigates the projected, quasi-symmetric $\sim\rm46\,kpc$-scale diffuse radio lobes surrounding the giant elliptical galaxy M\,87, utilizing well-sampled wideband ($\rm 60\,MHz-10.55\,GHz$) observations from MWA and VLA, supplemented by data from LOFAR and Effelsberg. The observed structures feature sharp edges and filaments, with nearly uniform and moderately steep spectral indices ($\alpha$, mostly within $-1.2\leq\alpha\leq-0.8$), indicating turbulence. Well-sampled radio spectra for the lobes' diffuse region are derived using the continuous injection (CI) model (with $\alpha_{\rm inj}\simeq-0.86$ and $\nu_{\rm b}\simeq1.72\rm\,GHz$), and for its three localized regions using the impulsive injection model (e.g., JP model). From energy equipartition analysis, we estimate the typical magnetic field strength in the lobes' diffuse region to be $B_{\rm eq}\simeq10\,\mu\rm G$. The age of the lobes is estimated as $\sim30-50\,\rm~Myr$, based on lifetimes derived from the CI and JP models and sound crossing time. Outflow powers of $\sim(0.2-2)\times10^{44}\,\rm erg\,s^{-1}$ for the lobes' diffuse components and $\sim(1-11)\times10^{44}\,\rm erg\,s^{-1}$ for the whole source are calculated. With this power assessment, we conclude that the galactic stellar wind has a negligible effect, the active galactic nucleus (AGN)-driven jet can provide the necessary energy for the whole system. Furthermore, we argue that while the wind driven by current AGN activity is unlikely to power the lobes' diffuse components, an average enhancement of AGN activity by a factor of $\sim 10^2$ over the past $\sim 30-50$ Myr remains plausible.

astro-ph.GA↗

arXiv2024

MeerKAT discovery of GHz radio emission extending from Abell 3017 toward Abell 3016

Context: The clusters Abell 3017 and Abell 3016 are located within a large-scale filament. A prominent X-ray bridge has been detected connecting the two clusters and a potential galaxy group between them. Aims: The aim of this work is to investigate the existence of a radio bridge in the filament between Abell 3017 and Abell 3016, to explore other diffuse radio structures within this system, and to investigate the origins of these diffuse radio emission. Methods: We analyzed MeerKAT L-band data to study the morphology and spectra of the diffuse radio structures in Abell 3016-Abell 3017. X-ray imaging and spectral analysis were carried out with archival Chandra and XMM-Newton data. Additionally, correlations between radio ($I_R$) and X-ray surface brightness ($I_X$) were generated to explore the connections between thermal and non-thermal components in the diffuse radio emission. Results: We detected a faint radio bridge with an average surface brightness of $\sim 0.1~\mu\rm Jy~arcsec^{-2}$ at 1280 MHz using MeerKAT. It connects Abell 3017 with a potential galaxy group and extends towards Abell 3016, aligning with the X-ray bridge. A high X-ray temperature of $7.09 \pm 0.54$ keV detected in the bridge region suggests an interaction between Abell 3017 and the group. In Abell 3017, we identified two distinct components of diffuse radio emission: a radio mini-halo and an outer radio halo with a northern extension (N-extension hereafter). The radio surface brightness profile of Abell 3017 shows a steep inner component consistent with other mini-halos, and a faint outer component likely linked to an infalling subcluster. The $I_{\rm R}-I_{\rm X}$ diagram indicates superlinear and sublinear correlations for the mini-halo and N-extension, respectively.

astro-ph.CO↗

arXiv2024

The warm-hot intergalactic medium in inter-cluster filaments -- A forecast for HUBS observations based on eRASS1 superclusters

Cosmological simulations indicate that nearly half of the baryons in the nearby Universe are in the warm-hot intergalactic medium (WHIM) phase, and about a half them reside in cosmic filaments connecting galaxy clusters. Recent observational studies using stacked survey data and deep exposures of galaxy cluster outskirts have detected soft X-ray excesses associated with optically identified filaments. However, the physical characteristics of WHIM in filaments remain largely undetermined due to a lack of direct spectral diagnostics of individual targets, which are limited by the spectral resolution of current instruments in the soft X-ray band. We aim to select appropriate targets for WHIM characterization through pointing observations with the future Hot Universe Baryon Surveyor (HUBS) mission, which is designed with eV-level energy resolution in the 0.1-2.0 keV band and a one-square-degree field of view, thus complementing other planned microcalorimetry missions such as Athena. We built a sample of 1577 inter-cluster filaments based on the eRASS1 supercluster catalog and estimated their soft X-ray emission. Their modeled emission and geometrical properties were used to select candidate targets for HUBS observations. Four inter-cluster filaments were selected as the most appropriate candidates. By simulating and analyzing their mock observations, we demonstrated that with 200 ks HUBS exposure for each candidate, the gas properties of individual filaments can be accurately determined, with the temperature constrained to $\pm0.01$ keV, metallicity constrained to $\leq\pm0.03$ solar, and density constrained to $<\pm10\%$. Elemental abundances of O, Ne, Mg, and Fe can be measured separately, providing unprecedented insights into the chemical history of the filament gas. We also show that direct mapping of the WHIM distribution is promising with narrowband imaging of the Oviii line.

astro-ph.GA↗

arXiv2024

An evaluation of source-blending impact on the calibration of SKA EoR experiments

Twenty-one-centimetre signals from the Epoch of Reionization (EoR) are expected to be detected in the low-frequency radio window by the next-generation interferometers, particularly the Square Kilometre Array (SKA). However, precision data analysis pipelines are required to minimize the systematics within an infinitesimal error budget. Consequently, there is a growing need to characterize the sources of errors in EoR analysis. In this study, we identify one such error origin, namely source blending, which is introduced by the overlap of objects in the densely populated observing sky under SKA1-Low's unprecedented sensitivity and resolution, and evaluate its two-fold impact in both the spatial and frequency domains using a novel hybrid evaluation (HEVAL) pipeline combining end-to-end simulation with an analytic method to mimic EoR analysis pipelines. Sky models corrupted by source blending induce small but severe frequency-dependent calibration errors when coupled with astronomical foregrounds, impeding EoR parameter inference with strong additive residuals in the two-dimensional power spectrum space. We report that additive residuals from poor calibration against sky models with blending ratios of 5 and 0.5 per cent significantly contaminate the EoR window. In contrast, the sky model with a 0.05 per cent blending ratio leaves little residual imprint within the EoR window, therefore identifying a blending tolerance at approximately 0.05 per cent. Given that the SKA observing sky is estimated to suffer from an extended level of blending, strategies involving de-blending, frequency-dependent error mitigation, or a combination of both, are required to effectively attenuate the calibration impact of source-blending defects.

astro-ph.IM↗

arXiv2024

Input gradient annealing neural network for solving low-temperature Fokker-Planck equations

We present a novel yet simple deep learning approach, called input gradient annealing neural network (IGANN), for solving stationary Fokker-Planck equations. Traditional methods, such as finite difference and finite elements, suffer from the curse of dimensionality. Neural network based algorithms are meshless methods, which can avoid the curse of dimensionality. However, at low temperature, when directly solving a stationary Fokker-Planck equation with more than two metastable states in the generalized potential landscape, the small eigenvalue introduces numerical difficulties due to a large condition number. To overcome these problems, we introduce the IGANN method, which uses a penalty of negative input gradient annealing during the training. We demonstrate that the IGANN method can effectively solve high-dimensional and low-temperature Fokker-Planck equations through our numerical experiments.

math.DS↗

arXiv2024

Advancing Noise-Resilient Twist Angle Characterization in Bilayer Graphene through Raman Spectroscopy via GAN-CNN Modeling

In this study, we introduce an innovative methodology for robust twist angle identification in bilayer graphene using Raman spectroscopy, featuring the integration of generative adversarial network and convolutional neural network (GAN-CNN). Our proposed approach showcases remarkable resistance to noise interference, particularly in ultra-low Signal-to-Noise Ratio (SNR) conditions. We demonstrate the GAN-CNN model's robust learning capability, even when SNR reaches minimal levels. The model's exceptional noise resilience negates the necessity for preprocessing steps, facilitating accurate classification, and substantially reducing computational expenses. Empirical results reveal the model's prowess, achieving heightened accuracy in twist angle identification. Specifically, our GAN-CNN model achieves a test accuracy exceeding 99.9% and a recall accuracy of 99.9%, relying on an augmented dataset containing 4209 spectra. This work not only contributes to the evolution of noise-resistant spectral analysis methodologies but also provides crucial insights into the application of advanced deep learning techniques for bilayer graphene characterization through Raman spectroscopy. The findings presented herein have broader implications for enhancing the precision and efficiency of material characterization methodologies, laying the foundation for future advancements in the field.

physics.app-ph↗

arXiv2023

Deep Learning Assisted Raman Spectroscopy for Rapid Identification of 2D Materials

Two-dimensional (2D) materials have attracted extensive attention due to their unique characteristics and application potentials. Raman spectroscopy, as a rapid and non-destructive probe, exhibits distinct features and holds notable advantages in the structural characterization of 2D materials. However, traditional data analysis of Raman spectra relies on manual interpretation and feature extraction, which are both time-consuming and subjective. In this work, we employ deep learning techniques, including classificatory and generative deep learning, to assist the analysis of Raman spectra of typical 2D materials. For the limited and unevenly distributed Raman spectral data, we propose a data augmentation approach based on Denoising Diffusion Probabilistic Models (DDPM) to augment the training dataset and construct a four-layer Convolutional Neural Network (CNN) for 2D material classification. Experimental results illustrate the effectiveness of DDPM in addressing data limitations and significantly improved classification model performance. The proposed DDPM-CNN method shows high reliability, with 100%classification accuracy. Our work demonstrates the practicality of deep learning-assisted Raman spectroscopy for high-precision recognition and classification of 2D materials, offering a promising avenue for rapid and automated spectral analysis.

physics.app-ph↗

arXiv2023

SUGAR: Spherical Ultrafast Graph Attention Framework for Cortical Surface Registration

Cortical surface registration plays a crucial role in aligning cortical functional and anatomical features across individuals. However, conventional registration algorithms are computationally inefficient. Recently, learning-based registration algorithms have emerged as a promising solution, significantly improving processing efficiency. Nonetheless, there remains a gap in the development of a learning-based method that exceeds the state-of-the-art conventional methods simultaneously in computational efficiency, registration accuracy, and distortion control, despite the theoretically greater representational capabilities of deep learning approaches. To address the challenge, we present SUGAR, a unified unsupervised deep-learning framework for both rigid and non-rigid registration. SUGAR incorporates a U-Net-based spherical graph attention network and leverages the Euler angle representation for deformation. In addition to the similarity loss, we introduce fold and multiple distortion losses, to preserve topology and minimize various types of distortions. Furthermore, we propose a data augmentation strategy specifically tailored for spherical surface registration, enhancing the registration performance. Through extensive evaluation involving over 10,000 scans from 7 diverse datasets, we showed that our framework exhibits comparable or superior registration performance in accuracy, distortion, and test-retest reliability compared to conventional and learning-based methods. Additionally, SUGAR achieves remarkable sub-second processing times, offering a notable speed-up of approximately 12,000 times in registering 9,000 subjects from the UK Biobank dataset in just 32 minutes. This combination of high registration performance and accelerated processing time may greatly benefit large-scale neuroimaging studies.

eess.IV↗

arXiv2023

Ram-pressure stripped radio tail and two ULXs in the spiral galaxy HCG 97b

We report LOFAR and VLA detections of extended radio emission in the spiral galaxy HCG 97b, hosted by an X-ray bright galaxy group. The extended radio emission detected at 144 MHz, 1.4 GHz and 4.86 GHz is elongated along the optical disk and has a tail that extends 27 kpc in projection towards the centre of the group at GHz frequencies or 60 kpc at 144 MHz. Chandra X-ray data show two off-nuclear ultra-luminous X-ray sources (ULXs), with the farther one being a plausible candidate for an accreting intermediate-mass black hole (IMBH). The asymmetry observed in both CO emission morphology and kinematics indicates that HCG 97b is undergoing ram-pressure stripping, with the leading side at the southeastern edge of the disk. Moreover, the VLA 4.86 GHz image reveals two bright radio blobs near one ULX, aligning with the disk and tail, respectively. The spectral indices in the disk and tail are comparable and flat ($\alpha > -1$), suggesting the presence of recent outflows potentially linked to ULX feedback. This hypothesis gains support from estimates showing that the bulk velocity of the relativistic electrons needed for transport from the disk to the tail is approximately $\sim 1300$ $\rm km~s^{-1}$. This velocity is much higher than those observed in ram-pressure stripped galaxies ($100-600$ $\rm km~s^{-1}$), implying an alternative mechanism aiding the stripping process. Therefore, we conclude that HCG 97b is subject to ram pressure, with the formation of its stripped radio tail likely influenced by the putative IMBH activities.

astro-ph.GA↗

arXiv2023

Applications of Raman Spectroscopy in Clinical Medicine

Raman spectroscopy provides spectral information related to the specific molecular structures of substances and has been well established as a powerful tool for studying biological tissues and diagnosing diseases. This article reviews recent advances in Raman spectroscopy and its applications in diagnosing various critical diseases, including cancers, infections, and neurodegenerative diseases, and in predicting surgical outcomes. These advances are explored through discussion of state-of-the-art forms of Raman spectroscopy, such as surface-enhanced Raman spectroscopy, resonance Raman spectroscopy, and tip-enhanced Raman spectroscopy employed in biomedical sciences. We discuss biomedical applications, including various aspects and methods of ex vivo and in vivo medical diagnosis, sample collection, data processing, and achievements in realizing the correlation between Raman spectra and biochemical information in certain diseases. Finally, we present the limitations of the current study and provide perspectives for future research.

physics.app-ph↗