## 1 Introduction  
Optical and spectral technologies offer many opportunities in intraoperative histopathology [1]. Among the techniques that are already approved or close to being applied in standard clinical practice for in vivo and ex vivo monitoring are Raman-based methods [2]. Raman spectroscopy probes inherent molecular vibrations without markers by inelastic scattering of monochromatic laser light. The general advantages are no or minimal sample preparation, no need for extrinsic markers, and high specificity of the spectrum of Raman-active vibrations. The main limitation of spontaneous Raman spectroscopy as a biomedical tool is due to the weak scattering cross-sections of biomolecules and the fluorescence emission of chromophores that overlap or obscure the Raman signals even in trace amounts. Several Raman variants were developed in the past decade to overcome this limitation, including (i) coherent Raman scattering [3, 4], (ii) time-gated Raman scattering [5], and (iii) shifted excitation Raman difference spectroscopy [6, 7] that are all experimentally complex, (iv) surface-enhanced Raman scattering [8, 9] which requires complex preparation protocols involving nanostructured surfaces or particles, and (v) data processing procedures [10; 11] that require expert knowledge in data science. An alternative approach that was developed more than two decades ago is spontaneous Raman spectroscopy with 1064 nm laser excitation. As this excitation wavelength is not absorbed by most chromophores, the acquired Raman spectra are minimally affected by the fluorescence background. The first technical realization used the Fourier transformation (FT) principle with a liquid nitrogen-cooled germanium-based detector for the registration of Raman spectra [12]. With the advent of dispersive spectrometers with holographic transmissive gratings and indium gallium arsenide (InGaAs) based array detectors, a new generation of versatile Raman instruments was developed that are compact, less expensive, and more sensitive than FT-Raman systems [13; 14]. In this work, a laser and spectrometer for 1064 nm Raman spectroscopy are coupled with a fiber-optic Raman probe, an inexpensive motorized stage, and a digital camera. As a first biomedical application, Raman images are collected from bladder tissue specimens for tumor identification. Bladder cancer is the 10 th most frequently diagnosed cancer worldwide (source: World Cancer Research Fund International [15]). Cystoscopy is the standard diagnostic method followed by excision of surgical biopsies and pathological inspection [16]. Raman spectroscopic-based techniques have been suggested as a complementary diagnostic tool to assess bladder tissues ex vivo in thin sections or biopsies and in vivo during cystoscopy. Raman papers in the context of bladder cancer between 2005 and 2022 have recently been summarized [17]. Of particular relevance for the current work was the characterization of bladder tissue using probe-based Raman spectroscopy [18]. As most Raman spectra in this study were affected by an intense background, a data processing workflow was presented to suppress the background. Subsequently, machine learning models distinguished tumor from non-tumor with sensitivity and specificity of 92% and 93%, respectively, and low-grade from high-grade tumor tissues with sensitivity and specificity of 85% and 83%, respectively. More recent work demonstrated the combination of a 2D spectrogram along with a convolutional neural network for the diagnosis of bladder cancer with an even higher accuracy of 99.2% [19]. Here, upscaling techniques were used to transform 1D Raman spectral data into a variety of 2D spectrograms. In addition, a weighted feature fusion network was constructed which was employed to evaluate multiple spectrograms. A translational approach introduced the combined use of in vivo Raman spectroscopy and in vivo cryoablation to detect and remove the residual bladder tumor during transurethral resection of bladder tumor (TURBT) [20].