<b>BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification</b> by BRISC Dataset (22559540)

“…It provides high-quality, physician-validated pixel-level masks and a balanced multi-class classification split, suitable for benchmarking segmentation and classification algorithms as well as multi-task learning research.</p><p dir="ltr">Highlights<br>- 6,000 T1-weighted MRI slices (5,000 train / 1,000 test)<br>- Four classes: Glioma, Meningioma, Pituitary Tumor, No Tumor<br>- Pixel-wise segmentation masks reviewed by radiologists<br>- Slices from three anatomical planes: Axial, Coronal, Sagittal<br>- Clean, stratified train/test splits and aligned image–mask filenames</p><h2> Dataset structure</h2><p dir="ltr"><b>brisc2025/</b><br>├─ classification_task/<br>│ ├─ train/<br>│ │ ├─ glioma/<br>│ │ │ ├─ brisc2025_train_00001_gl_ax_t1.jpg<br>│ │ │ └─ ...…”

Experiment. by He Ren (139498)

Data_Sheet_1_Improving Crowdsourcing-Based Image Classification Through Expanded Input Elicitation and Machine Learning.PDF by Romena Yasmin (12970919)

“…<p>This work investigates how different forms of input elicitation obtained from crowdsourcing can be utilized to improve the quality of inferred labels for image classification tasks, where an image must be labeled as either positive or negative depending on the presence/absence of a specified object. …”

Imaging parameters. by Reihaneh Hassanzadeh (11986041)

“…We used confounder-controlled rs-FNC and applied machine learning algorithms (including support vector machine, logistic regression, random forest, and k-nearest neighbor) and deep learning models (i.e., fully-connected neural networks) to classify subjects in binary and three-class categories according to their diagnosis labels (e.g., AD, SZ, and CN). …”

Supplementary Material for: Utilizing Deep Learning to Identify Electron-Dense Deposits in Renal Biopsy Electron Microscopy Images by figshare admin karger (2628495)

“…The ResNet18 architecture was selected for our task. To evaluate the model's classification capability, we created a binary classification model to identify the presence of deposits in EM images. …”

UMAHand: Hand Activity Dataset (Universidad de Málaga) by Eduardo Casilari (3333906)

“…<p dir="ltr">The objective of the UMAHand dataset is to provide a systematic, Internet-accessible benchmarking database for evaluating algorithms for the automatic identification of manual activities. …”

Participants’ demographic characteristics. by Reihaneh Hassanzadeh (11986041)

“…We used confounder-controlled rs-FNC and applied machine learning algorithms (including support vector machine, logistic regression, random forest, and k-nearest neighbor) and deep learning models (i.e., fully-connected neural networks) to classify subjects in binary and three-class categories according to their diagnosis labels (e.g., AD, SZ, and CN). …”