~/yuki-kashiwada

$ ls research/

Research

Peer-reviewed papers and public datasets on medical AI. Each page has a summary, key numbers, an FAQ, and citation formats (APA / BibTeX / RIS).

ArticleFirst author

Applying vision transformer to assess multi-scale morphological features in mammography for breast cancer detection: multiscale image morphological extraction vision transformer (MIME-ViT)

Y. Kashiwada, E. Takaya, M. Hiroya, et al. — PeerJ Computer Science, 11:e3252

MIME-ViT fuses four Vision Transformer branches with different patch sizes and a CNN branch to localize masses and calcifications on mammograms. Trained from scratch on the TOMPEI-CMMD annotations, it reached a mean IoU of 0.3342: better than DETR without pre-training, but below pre-trained DETR and YOLOv8x.

Mean IoU (all lesions)
0.3342
Mean IoU (mass)
0.3797
Mean IoU (calcification)
0.2491

Read the summary: MIME-ViT →DOI: 10.7717/peerj-cs.3252 ↗

DatasetFirst creator

TOMPEI-CMMD Dataset

Y. Kashiwada, E. Takaya, M. Hiroya, et al. — The Cancer Imaging Archive (TCIA)

TOMPEI-CMMD adds polygon lesion segmentations (mass, calcification, and four other finding types), lesion locations, corrected laterality/view labels, and clinical data to the Chinese Mammography Database (CMMD). It contains 1,773 masks for 1,385 breasts from 1,363 patients and is published on TCIA under CC BY 4.0.

Patients
1,363
Breasts with lesions (MLO)
1,385
Segmentation masks
1,773

Read the summary: TOMPEI-CMMD →DOI: 10.7937/wezw-bh22 ↗

ArticleSecond author (co-author)

Differentiation between epileptic and functional/dissociative seizures using density spectral array of ictal single-channel EEG with deep learning

K. Konomatsu, Y. Kashiwada, T. Kubota, et al. — Epilepsy & Behavior, 172:110713

Ictal EEG was turned into time-frequency images (density spectral arrays, DSA) and a ResNet34 model was trained to tell mesial temporal lobe epilepsy (mTLE) seizures from functional/dissociative seizures (FDS). Among reduced-electrode settings, the single Cz channel gave the highest AUC, 0.941.

AUC (single channel Cz)
0.941
Patients
99
Seizures analysed
206

Read the summary: Single-channel EEG DSA for seizure differentiation →DOI: 10.1016/j.yebeh.2025.110713 ↗