matlab-toolbox-skills

MATLAB Deep Learning Toolbox (R2025b). Functions - trainnet, trainingOptions, unet, unet3d, deeplabv3plus, semanticseg, yolov4ObjectDetector, fasterRCNNObjectDetector, maskrcnn, resnet50, efficientnetb0, imagePretrainedNetwork, dlarray, dlfeval, dlgradient, adamupdate, dlnetwork, imageDatastore, augmentedImageDatastore, minibatchqueue. Tasks - train a deep learning model, classify medical images, build a CNN classifier, segment tumors or organs, detect objects or nodules, fine-tune a pretrained network, transfer learning, create a U-Net, train with custom loss, augment training data, deploy model to ONNX, run training on GPU, build a 3D volumetric network, handle class imbalance. Domains - MRI, CT, X-ray, histopathology, dermatology, retinal imaging, cell detection, lesion segmentation, nodule detection, industrial inspection, autonomous systems, satellite imagery, general computer vision, defect detection, quality control imaging.

MATLAB Image Processing Toolbox. Functions - imgaussfilt, medfilt2, wiener2, imfilter, graythresh, imbinarize, multithresh, watershed, activecontour, strel, imopen, imclose, imerode, imdilate, bwareaopen, imfill, regionprops, bwconncomp, bwlabel, edge, im2double, im2uint8, mat2gray, adapthisteq, imadjust, blockproc. Tasks - remove noise from an image, filter a noisy image, smooth an image, enhance contrast, threshold an image, segment objects, separate touching objects, clean up a binary mask, fill holes in mask, remove small objects, count cells or particles, measure region properties like area and centroid, detect edges, convert image data types, preprocess images before deep learning, apply morphological operations, extract texture features, process large images in blocks. Domains - MRI preprocessing, CT windowing, microscopy, histology, cell counting, fluorescence imaging, image segmentation, satellite imagery, industrial inspection.

MATLAB Medical Imaging Toolbox. Functions - medicalVolume, dicomread, dicominfo, dicomCollection, niftiread, niftiinfo, nrrdread, medicalref3d, intrinsicToWorld, worldToIntrinsic, volshow, sliceViewer, imregmoment, imregdeform, imregtform, radiomics, intensityFeatures, shapeFeatures, textureFeatures, medicalSegmentAnythingModel, extractEmbeddings, segmentObjectsFromEmbeddings, dicomConnection, dicomquery, dicomget. Tasks - load medical scans, read DICOM series, open NIfTI or NRRD files, convert patient and voxel coordinates, visualize 3D volumes, overlay segmentation, align MRI or CT scans, register pre and post treatment images, extract radiomics features, segment with MedSAM, segment cells in microscopy, connect to PACS server, label ground truth, resample to isotropic spacing. Domains - DICOM, NIfTI, NRRD, MRI, CT, PET, PET/CT fusion, ultrasound, X-ray, brain imaging, liver segmentation, cardiac imaging, lung nodules, tumor analysis, clinical workflows, PACS integration, microscopy, cell counting.

MATLAB Statistics and Machine Learning Toolbox. Functions - fitcsvm, fitctree, fitcensemble, fitcknn, fitcnb, fitcnet, fitlm, fitglm, fitrgp, fitrensemble, lasso, kmeans, linkage, fitgmdist, dbscan, pca, tsne, factoran, ttest, ttest2, anova1, anovan, ranksum, chi2gof, fitdist, makedist, mle, ecdf, coxphfit, cvpartition, crossval, kfoldLoss, perfcurve, confusionchart, bayesopt, shapley, normalize, fillmissing. Tasks - run t-test or ANOVA, fit distributions, classify patients, train SVM or random forest, predict with regression, cluster data, reduce dimensions with PCA or t-SNE, cross-validate, compute ROC and AUC, select features, optimize hyperparameters, analyze survival data, plot Kaplan-Meier curves, handle missing data, interpret with SHAP or LIME. Domains - biomarker discovery, clinical trials, survival analysis, diagnostic classification, gene expression, epidemiology, predictive maintenance.

MATLAB Wavelet Toolbox. Functions - wavedec2, waverec2, dwt2, idwt2, swt2, lwt2, ilwt2, wdenoise2, dualtree2, idualtree2, shearletSystem, liftingScheme, liftingStep, addlift, wfilters, wmaxlev, dldwt, dlidwt, cwtLayer, appcoef2, detcoef2. Tasks - decompose an image into frequency bands, denoise a medical image using wavelets, remove noise from MRI or CT or ultrasound, extract texture features at multiple scales, design a custom wavelet, learn wavelets from data, detect edges and orientations, analyze directional structures like vessels or fibers, fuse multi-modal images, compress an image with wavelets, build wavelet layers for deep learning, choose the right wavelet for my image type, verify perfect reconstruction. Domains - MRI denoising, CT noise reduction, ultrasound speckle removal, image fusion, texture classification, wavelet-based feature extraction, medical image preprocessing, image compression.